QuilrAI

No entries match these filters. Clear filters

1. 1.0.0 Oct 19, 2026 · issue 113

   QuilrAI now publishes an API — 1 endpoint across 1 area: Api⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

   • Api (1 endpoint) — read⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

   1 detail in Everything

2. docs update Oct 8, 2026 · issue 097

   QuilrAI adds a Detection Models page with configurable risk thresholds for data and AI adversarial checks across LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code. and MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once. policies.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

   └──▷ HOW TO FIND IT
   Tune PIIPIIPersonally Identifiable Information — data that can identify a specific individual, such as names, email addresses, or SSNs. Cyber tools flag or redact it to limit exposure and meet privacy regulations. detection sensitivity so that low-signal signals like a person's name alone are caught and routed through Policy Engine rules.
   📍In the QuilrAI Console, go to Govern › Detection Models › Data Risks › Out-of-the-box › Contextual. Find the PII category row, open its Risk level dropdown and change it from Low to High. Expand PII to confirm the name subcategory (priority: Low) is now included in the detection scope.
   Verify a jailbreak detection fires on a suspect prompt before wiring it into a live guardrail policy.
   📍In the QuilrAI Console, go to Govern › Detection Models › AI Adversarial Risks. Find the Jailbreak group, click Show techniques to review individual techniques, then click View & test. Paste a synthetic adversarial prompt and confirm the risk-level badge and technique match before referencing this detection in a Policy Engine rule.
   • Adds the Detection Models page under Govern in the QuilrAI Console, with two tabs — Data Risks and AI Adversarial Risks — as the central place to define what Quilr scans for and the default risk level of each finding.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
   • Adds Out-of-the-box and Custom views under Data Risks, where built-in detections are split into Contextual (uses surrounding text) and Non-contextual (matches by format) sub-views.
   • Adds contextual detection categories — PII, PHIPHIProtected Health Information: individually identifiable health data regulated under the US HIPAA law, whose strict handling requirements drive cyber tools to audit, mask, or restrict access to it., PFI, PCIPCIA hardware bus standard that connects peripheral devices to a computer's motherboard; cyber tools targeting hardware-level forensics or memory analysis use it to access device registers and DMA regions directly., Insurance Data, Auth & Secrets, Code Scripts and Queries — each with a configurable Risk level (None, Low, Medium, High) that acts as a detection threshold: None detects nothing, Low fires only on High-priority subcategories, Medium on High and Medium, High on all three.
   • Adds non-contextual detections for fixed-format entities — SSN, Aadhaar, PAN, passport number, phone number, email address, IBAN, UPI ID, card number, and CVV — each with an Enabled toggle.
   • Adds AI Adversarial Risks tab covering Prompt Injection, Jailbreak, Prompt Context Corruption, Semantic Adversarial Prompts, Social Engineering Prompts, and Response Risks, each showing a risk-level badge and a Show techniques drill-down.

   +2 more show less

   • Adds View & test action on every category and adversarial group, letting practitioners run sample text against a detection before referencing it in a policy.
   • Establishes a precedence model: an LLM Gateway app's Guardrails Risk level overrides the org-wide Detection Models setting, and when the Policy Engine is enabled the policy wins over both.

   7 details in Everything

3. docs update Oct 8, 2026 · issue 097

   QuilrAI Policy Engine gains a full draft-to-publish workflow with simulation, replay, and rollback on LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code. and MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. Gateways.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

   └──▷ HOW TO FIND IT
   Verify a new scope-limited block rule before it touches live traffic by simulating a positive and negative case in the Policy Engine.
   📍1. On the Data & Adversarial Risks card, select Configure › add a Smart group scope for Contractors, pick Auth & Secrets, set action to block. 2. Select Describe a request. Add a user in Contractors and use Add detection to report Auth & Secrets — card should resolve to block. 3. Repeat with a user outside Contractors — card should resolve to monitor (or the prior value). If it shows block, the scope is wrong.
   Measure blast radius of a draft rule against 30 days of real traffic before publishing, to catch unintended blocks.
   📍In the Policy Engine, with your draft open, select Replay › set window to 30 days › run. Inspect the would-be-blocked set to confirm only Contractors calls carrying Auth & Secrets appear.
   Author a block rule in QuilrQL source for bulk or complex policy work that the card editor cannot express.
   yaml copy

   policy block_request_secrets priority 900 {
     request
     data_type ("Auth & Secrets")
     then {
       dlp.action = block;
       risk.level = critical;
     }
   }

   • Adds Describe a request (MCP: Describe a call) simulation in the Policy Engine to show the winning value per control for a single request, with Add detection reporting a data type as found with a count per type.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
   • Adds synthetic-case simulation: run up to 100 synthetic cases against a draft to read the engine's actual decision evidence — which rules matched, on which stage, and the call-level outcome.
   • Adds Replay of recorded traffic against a draft: up to 90 days and 5,000 requests on the LLM Gateway, with filters by window, MCP, tool, user, and route on the MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once..
   • Adds a shared draft workflow on the LLM Gateway and MCP Gateway tabs of the Policy Engine, where every card edit from every admin collects in one draft with a concurrency guard preventing silent overwrites.
   • Adds Scope shortcuts on cards to add a User, Smart group, or Application condition with seeded priorities (900, 700, and 600) so narrower scopes win without manual numbering.

   +6 more show less

   • Adds Advanced policies > Advanced workspace with New draft from active revision, Suggested policies, Insert from catalog, Save draft, and an 02 Analyze step running Diagnostics, Simulation, and Historical Try before publishing.
   • Adds a </> toggle on every policy to switch between the sentence editor and QuilrQL source view in both directions.
   • Adds numbered revision publishing: all pending changes publish together as the next Revision N; LLM Gateway publish accepts a message; MCP Gateway drafts can be renamed.
   • Adds rollback: any earlier revision can be republished as a new revision, keeping the timeline forward-only and fully auditable, without affecting classic app settings.
   • Supports condition operators including is inside CIDR, matches pattern, has entry, has count, is all of, and occurrence thresholds on Data found fields in the card editor.
   • Adds an activity view rolling up governed calls, blocked calls, and tokens saved over the last 7 or 30 days; MCP Gateway attributes match counts per policy, while LLM Gateway reports tenant totals only.

   11 details in Everything

4. docs update Oct 8, 2026 · issue 097

   QuilrAI adds AI-designed custom dashboards with KPI/chart widgets, filters, caveats, and shareable ?dashboard= links.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

   └──▷ TRY IT
   Share a specific dashboard directly with a teammate who has access, without them having to search for it.
   copy

   $ https://app.quilrai.dev/console/observe/dashboards?dashboard=<dashboard-id>

   Create a new AI-designed dashboard from the QuilrAI console when you need a custom view of your data.
   📍1. Go to Observe › Dashboards (page titled Custom dashboards). 2. Click + Create dashboard. 3. Describe what you want to see; the AI designer asks follow-up questions. 4. Review the preview built on your own data. 5. Publish, choosing Organization visibility or keeping it private.
   • Adds ?dashboard= query parameter to dashboard URLs, enabling direct sharing of a specific dashboard with anyone who has access.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
   • New Observe > Dashboards section (titled Custom dashboards) lets practitioners build dashboards by describing them in plain language to an AI designer, which asks follow-up questions and generates a preview on live data.
   • Dashboards support KPI and chart widgets, a Filters panel with time-period selection and Apply filters, a Caveats section noting data coverage, and a Revise with AI prompt to iteratively update the layout.
   • Dashboards can be published with Organization-wide or private visibility, and an Export option plus a more-actions menu are available on each dashboard.

   4 details in Everything

5. docs update Oct 8, 2026 · issue 097

   QuilrAI adds an Agents view under Observe showing every AI agent, its skills, MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. servers, system prompts, models, and users across your estate.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

   └──▷ HOW TO FIND IT
   Find risky or oversized system prompts across all observed agents to prioritise prompt hygiene work.
   📍In the console, go to Observe › Agents, open the System prompts tab, and sort by Risk or token size to surface prompts flagged with high risk or the largest compression savings.
   Investigate which AI agents are reaching an MCP server and whether any calls are being blocked, to audit lateral-reach exposure.
   📍In the console, go to Observe › Agents, open the MCP servers tab, and review the Used by, Calls, Tools, and Blocked columns for each server.
   Audit a specific user's agent activity — agents run, call volume, and blocked calls — to investigate anomalous behaviour.
   📍In the console, go to Observe › Agents, open the Users tab, locate the person of interest, and review their agents used, calls, blocked calls, and devices columns.
   • Adds Observe > Agents screen listing every AI agent observed across the estate, with columns for Source, Capabilities (observed and declared), Calls, Users, Governance, and Last seen.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
   • Adds source-filter chips for Azure AI Foundry, Copilot Studio, Endpoint discovered, Endpoint runtime, MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once., and OpenAI Compliance to scope the agent inventory.
   • Adds headline tiles for Agents observed, Observed calls, People, and MCP reach (with blocked-call counts) at the top of the Agents view.
   • Adds Skills tab showing which skills agents use and how widely, as read-only usage evidence.
   • Adds MCP servers tab showing which agents call which MCP servers, with columns for Used by, Evidence, Calls, Users, Tools, and Blocked.

   +3 more show less

   • Adds System prompts tab showing observed prompts with token size, potential compression savings, risk rating, and a top suggestion per prompt.
   • Adds Users tab listing the people who run agents, with per-person columns for agents used, calls, blocked calls, and devices.
   • Adds a per-agent drawer with five tabs: Overview, Users, Dependencies & Calls, Activity, and System prompt, surfacing identity, type, parent applications, sources, top users, and source instances.

   8 details in Everything

6. docs update Oct 8, 2026 · issue 097

   QuilrAI gains an Observe > Users hub with five tabs covering AI usage, risk, deployment coverage, accounts, and Quilly coaching feeds.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

   └──▷ HOW TO FIND IT
   Spot employees conducting work in personal AI accounts and immediately queue a Quilly agent to follow up on their findings.
   📍In the console, go to Observe › Users › Accounts. Filter the Account type column to Personal. Select up to 50 accounts and choose Activate Agent.
   Audit Browser Extension rollout health — find users on stale extension versions or missing the Browser Utility before they create a coverage gap.
   📍In the console, go to Observe › Users › Browser deployment. Review the per-user table for version lag and Browser Utility status. Use Validate deployment for remediation steps.
   Investigate off-hours AI activity for a specific user by opening their full profile and reviewing LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code. Gateway requests.
   📍In the console, go to Observe › Users › All users. Filter by Off-hours actors. Click Full profile on the person card, then navigate to LLM Gateway › Requests.
   • Adds Observe > Users page with five tabs — All users, Browser deployment, Endpoint deployment, Accounts, and Quilly — giving a per-person view of AI usage, risk, and sensor coverage.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
   • Adds All users tab with headline tiles (People, Flagged people, Off-hours actors, Activity) and per-person cards showing apps, topics, posture per sensor, usage, active hours, risk, and origin.
   • Adds Cards and Table layout toggle, saved views, Add filter, Refresh users, Columns, and Export controls on the All users tab.
   • Adds a Full profile link on each person card that opens a profile drawer with sensor, directory, extension, and endpoint status, plus left-nav sections: Overview, Graph, Interactions, Inventory, Accounts, Origin & access, and gateway sub-tabs.
   • Adds LLM Gateway sub-tabs (Connections, Requests, Usage & cost) inside the person profile drawer.

   +6 more show less

   • Adds MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once. sub-tabs (Connections, Tool calls) inside the person profile drawer.
   • Adds Browser deployment tab showing Browser Extension coverage, version lag, browser types in use, and Browser Utility status per user.
   • Adds Endpoint deployment tab with headline tiles (Workstations, Enabled, Disabled, Without persona, Latest version) and a table of Workstation, User, OS, Agent version, Status, and Last registered, with bulk Enable / Disable actions.
   • Adds Accounts tab listing AI accounts people use with Account type (Personal or Corporate), approval status, risk, activity, and first/last observed, supporting bulk selection of up to 50 accounts to Activate Agent.
   • Adds Quilly tab as a feed of Quilly AI coach conversations split into Findings, Coach, and User initiated sub-feeds, each entry showing summary, user, control, status, action taken, tools used, and severity.
   • Adds Activate Agent bulk action on both Accounts tab and All users tab to have a Quilly agent follow up on selected accounts or people's findings.

   11 details in Everything

7. docs update Oct 8, 2026 · issue 097

   QuilrAI adds Findings & Interactions hub with investigation drawer, saved views, bulk triage, agent activation, and export center.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

   └──▷ HOW TO FIND IT
   Audit a specific person's AI activity over the past week without leaving your filtered list — open the investigation drawer to replay exactly what they did and which policy fired.
   📍In the console, go to Observe › Findings & Interactions. Select the Interactions tab, set the date menu to Last 7 days, type the person's name in the search box, then click any row to open the investigation drawer. Open the Prompts & completions or Capture tab and use Flagged only to focus on policy hits. Click Copy thread to capture the transcript for your ticket.
   Send follow-up messages to the people behind a batch of open findings — for example, to explain the policy they triggered and ask them to remediate — without manually contacting each one.
   📍In the console, go to Observe › Findings & Interactions. Use the Open toggle to show unresolved findings, apply Category and Outcome filters to narrow the set, then select up to 50 finding cards and click Activate Agent. In Configure agent, choose the Agent, set the Policy, Tone, Reminders (0-3), and Resolve within (e.g. 48 hours), then click Generate plan. Review the drafted plan, confirm each finding shows Ready, and click Start agent.
   Persist a compliance-audit filter combination so the same filtered view of blocked findings from the LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code. Gateway is one click away every morning.
   📍In the console, go to Observe › Findings & Interactions. On the Findings tab, set Sensor to LLM Gateway and Outcome to Blocked, choose the Table layout, and click Columns to add the columns you need. Then click Save new view and name it. The saved view appears as a chip next to All findings for instant access.
   • Adds Findings & Interactions page (under Observe > Findings & Interactions in the sidebar) as a unified hub showing AI activity across all sensors, with Findings and Interactions tabs.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
   • Adds Bird's-eye, Cards, and Table layout buttons on Findings & Interactions to switch between a trend-summary view, a card queue, and a sortable row-per-item table — filters and the selected period carry across all three.
   • Adds Group related conversations toggle on the Interactions tab to fold related records (such as multiple prompts in one short session) into a single row.
   • Adds quick filters on Findings — Sensor, Person, App, Category, and Outcome — and on Interactions — Sensor, Person, App, Model, and Tool — plus a More filters panel that includes Triage status, Triage reason, Topic, Agent, and Device.
   • Adds All / Open / Resolved toggle to show findings by triage state; Open surfaces the queue still awaiting a decision.

   +4 more show less

   • Adds Save new view to persist a combination of filters, columns, and layout as a chip beside All findings for one-click return.
   • Adds investigation drawer (opened by clicking any card or row) with Summary, activity, Sensitive data, Context, and Details tabs; the activity tab is named by sensor — Capture (browser), Prompts & completions (LLM Gateway), Tool calls (MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once.), or Agent activity (Endpoint Agent).
   • Adds Reveal values in the investigation drawer to show masked sensitive-data values in clear for authorized roles; each reveal is logged in Audit logs as Requested sensitive data reveal, and Copy thread is logged as Requested conversation copy.
   • Adds Export action on Findings & Interactions that sends the filtered list to the Export Center for download.

   9 details in Everything

8. docs update Oct 8, 2026 · issue 097

   QuilrAI adds an interactive Graph view mapping AI estate relationships across people, agents, MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. servers, data, and controls.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

   └──▷ HOW TO FIND IT
   Trace which agents and MCP servers a specific person can reach, starting from their profile.
   📍In the console, go to Users › [select a person] › Graph section to open that person's relationship map showing their AI footprint.
   Audit all agentic relationships in the estate — e.g. which agents reach a given MCP server — using a focused lens and sensor filters.
   📍In the console, go to Observe › Graph, select the Agentic lens, then use the sensor chips to toggle MCP and LLM data sources, and click expand on the MCPs category node to follow relationships.
   Review an asset's connections to people, agents, and controls directly from Inventory.
   📍In the console, go to Inventory › [open an asset drawer] › Graph section to see that asset's relationship view.
   • Supports five graph lenses — Estate, People, Agentic, Risk, and Governance — each emphasising a different relational question.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
   • Adds sensor chips to show or hide data from LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code., MCP, Endpoint, Browser, Copilot, GitHub, Foundry, and OpenAI sources within the graph.
   • Surfaces a central Organization node with category nodes for Apps, People, Agents, MCPs, Skills, Models, Data, Devices, Plugins, Hooks, Permissions, Tool families, and Work (working directories and repositories).
   • Adds a Period selector and + Add filter control to narrow the graph view.
   • Embeds the same relationship Graph section inside individual person profiles under Users and inside asset drawers in Inventory.

   5 details in Everything

9. docs update Oct 8, 2026 · issue 097

   QuilrAI console gains deep-link URL parameters, Bird's-eye layout, Export Center, and saved views across all list pages.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

   └──▷ HOW TO FIND IT
   Open the Findings filter panel instantly from the keyboard to narrow results without reaching for the mouse.
   📍Press F on any Findings & Interactions list page to open the full filter panel, then select fields such as Sensor, Person, App, Category, or Outcome.
   • Adds ?person=, ?asset=, ?agent=, ?interaction=, ?dashboard=, ?workspace=, and ?manage= URL parameters that open the corresponding detail drawer directly, enabling shareable deep links to any drawer.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
   • Adds a Bird's-eye layout on Findings & Interactions that shows summary tiles and charts for the current filters before drilling into rows.
   • Adds an Export Center where exports triggered from any list page are collected, recurring exports are configured, and past runs are downloaded.
   • Adds saved views that remember filters, columns, and layout for any list page, accessible as chips above the list via Save new view.
   • Adds a keyboard shortcut (F) to open the full filter panel from anywhere on a list page.

   +2 more show less

   • Adds Compare: previous period option on pages such as Costs & Savings to show changes against the prior period.
   • Adds a More filters / Filters > Add filter panel exposing the full field list per page, including sensor-specific fields.

   7 details in Everything

10. docs update Oct 8, 2026 · issue 097

    QuilrAI adds a Syslog integration that forwards Extension findings via TCPTCPA network protocol that delivers a reliable, ordered stream of bytes between two endpoints. It detects lost data and retransmits it., UDPUDPA network protocol that sends individual messages without guaranteeing delivery or order. Applications trade built-in reliability for less overhead and can add their own recovery., or TLS in RFC 5424RFC 5424The IETF standard that defines the Syslog protocol's message format, severity levels, and transport rules, giving cyber tools a common structured log schema to parse and forward system events reliably. or RFC 3164RFC 3164The 2001 IETF specification that standardised the BSD syslog protocol, defining message format and UDP transport for system log data. Cyber tools use it to parse or emit legacy syslog reliably across heterogeneous infrastructure. format.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Forward QuilrAI DLPDLPData Loss Prevention: a category of controls that inspect and block sensitive data from leaving an organisation via email, web, or removable media, giving cyber tools a policy enforcement layer over data in motion and at rest. findings to a TLS-secured syslog server using RFC 5424 framing with octet-counting, so rsyslog can reliably parse multi-line messages.
    📍In QuilrAI, go to Integrations › Available tab › click + Add on the Syslog tile, then fill in: Syslog Server IP/Domain: syslog.corp.example.com Syslog Server Port: 6514 Protocol: TLS Message Format: RFC 5424 Message Framing: Octet-Count
    • Adds a Syslog integration supporting TCP, UDP, and TLS transport protocols to forward Extension findings to any syslog server.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Supports RFC 5424 (default) and RFC 3164 message formats, configurable per connection.
    • Supports TCP Message Framing options — None (default), Octet-Count (RFC 6587 octet-counting), and Non-Transparent (RFC 6587 trailing-newline) — for compatibility with receivers such as rsyslog and syslog-ngsyslog-ngAn open-source log collector and router that ingests, filters, parses, and forwards log messages from many sources to many destinations, giving cyber tools a flexible, high-throughput pipeline for centralizing event data..
    • Delivers findings as a structured JSON payload under the quilr-siem-event app name, including browser context (name, version, os), user identity (username, accountname), application (name, url), and DLP check detail (id, name, mode, action_name, detections_original, detections_final).

    4 details in Everything

11. docs update Oct 8, 2026 · issue 097

    QuilrAI adds a Triage Center with bulk-close suggestions, auto-resolve rules, activity undo, and detection tuning via learnings.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Suppress recurring false positives org-wide so the same low-risk content stops filling your findings queue.
    📍In the console, go to Observe › Triage center › Detection tuning. Click Run now to generate fresh learnings, then open To review. For each learning card, read the explanation, check Findings would be cleared, click Show examples to verify, tick the learnings you agree with, and click Apply learnings. Applied learnings resolve all matching open findings as False positive and suppress that content going forward.
    Automatically close findings about test accounts without manual review, so your team only sees findings that need action.
    📍In the console, go to Observe › Triage center › Auto-resolve rules. Click Create a rule, select the saved condition that matches test-account findings, set the action to Resolve, and save. Choose whether the rule should also apply to already-open findings matching the condition. The rule runs in list order and logs every change to the Activity tab.
    Bulk-close a batch of stale findings and immediately undo if the wrong findings were captured.
    📍In the console, go to Observe › Triage center › Suggestions. Open a suggestion card (e.g. People no longer active), review the counts and Largest finding groups list, then click Resolve. To roll back, open the Activity tab, locate the bulk-close entry, and click Undo — this reopens every finding the bulk close resolved, except those changed manually afterwards.
    • Adds Auto-resolve rules tab to automatically resolve, review, or tag new open findings that match a condition, running rules in list order with every change logged in Activity.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Adds Suggestions tab for bulk-closing open findings grouped by reason (e.g. apps now approved, people no longer active, blocked for 14 days), with a re-checked count and full undo from Activity.
    • Adds Activity tab listing every bulk close, auto-resolve rule change, and undo with filters for period, type (All / Suggestion / Undo), actor, and reason — plus View findings and Undo actions per row.
    • Adds Detection tuning tab with Quick suggestions that generate plain-language learnings from recent findings; applying a learning resolves all matching open findings as False positive and suppresses the same content going forward.
    • Adds Run now and Auto (automatic daily schedule with configurable Time of day, Time zone, and Findings per run) controls for detection-tuning suggestion runs.

    +3 more show less

    • Adds Open learnings and Findings would be cleared counters in the Detection tuning header to preview impact before applying.
    • Supports three triage states — Open, In review, and Resolved — filterable via the All / Open / Resolved toggle and the Triage status filter on the Findings page.
    • Adds headline tiles (Suggestions ready, Resolved, Suggested to resolve, Resolved by reason) with a 30-day / 90-day period toggle for queue overview.

    8 details in Everything

12. docs update Oct 8, 2026 · issue 097

    QuilrAI adds a Google Workspace MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. with 42 tools spanning Gmail, Calendar, Drive, and Directory over customer-owned OAuth.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ USE IT
    Opt in to Drive file-sharing for your deployment so agents can change file permissions via share_google_drive_file.
    .env copy

    GOOGLE_ENABLE_DRIVE_SHARING=true

    Install Google Workspace from the MCP Library and connect tenant-owned OAuth so agents can reach all four surfaces under one governed gateway boundary.
    📍In QuilrAI, go to Settings › AI Gateway › MCP Gateway, click Library, find Google Workspace, click Set up, enter the OAuth Client ID and Client Secret from Google Cloud Console, click Install, then Connect OAuth as an administrator and grant all 15 scopes at Google's consent screen.
    • Enables GOOGLE_ENABLE_DRIVE_SHARING=true environment variable to activate the otherwise-disabled share_google_drive_file tool for Drive permission changes.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Adds share_google_drive_file tool (disabled by default, destructive) for changing who can access a Google Drive file; grants to anyone require explicit confirmation.
    • Adds Gmail tools: search_gmail_messages, get_gmail_messages, get_gmail_thread, read_gmail_attachment, list_gmail_drafts, get_gmail_draft, list_gmail_labels, sync_gmail_messages, save_gmail_draft, save_gmail_label, update_gmail_messages, send_gmail_message, trash_gmail_messages, delete_gmail_label, delete_gmail_draft.
    • Adds Google Calendar tools: list_google_calendars, list_google_calendar_events, get_google_calendar_event, list_google_calendar_event_instances, get_google_calendar_availability, sync_google_calendar_events, save_google_calendar_event, save_google_calendar, respond_to_google_calendar_event, delete_google_calendar_event, delete_google_calendar.
    • Adds Google Drive tools: list_google_drive_files, get_google_drive_file, list_google_shared_drives, read_google_drive_file, export_google_drive_file, list_google_drive_comments, list_google_drive_revisions, list_google_drive_permissions, sync_google_drive_changes, create_google_drive_file, save_google_drive_file, create_google_drive_comment, trash_google_drive_file.

    +5 more show less

    • Adds Workspace Directory tools via the People API: get_google_user_profile, search_google_directory, list_google_directory_people (read-only, own domain only, no Admin SDK).
    • Supports incremental sync across Gmail (sync_gmail_messages), Calendar (sync_google_calendar_events), and Drive (sync_google_drive_changes) via sync-marker-based tools.
    • Tags seven default-enabled destructive tools (send_gmail_message, trash_gmail_messages, delete_gmail_label, delete_gmail_draft, delete_google_calendar_event, delete_google_calendar, trash_google_drive_file) with the destructive annotation so operators can hide or require human approval as a class.
    • Requires 15 Google OAuth scopes including restricted drive, gmail.readonly, gmail.compose, gmail.modify, and sensitive calendar.* and directory.readonly scopes; tokens lacking openid and userinfo.email are rejected outright.
    • Provides structured JSON error responses with code, HTTP status, retryable flag, and next_action field for all tool failures.

    10 details in Everything

13. docs update Oct 8, 2026 · issue 097

    QuilrAI MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once. sits between AI apps and MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. servers, enforcing tool rules, guardrails, and logging every call by user and agent.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Point an AI client at your MCP Gateway instead of a raw MCP server to enforce guardrails and log every tool call.
    📍In your AI client (e.g. Cursor or VS Code), replace the MCP server URL with your Quilr gateway URL, e.g. https://mcpgateway.quilr.ai/github/mcp, and add your API token as the bearer credential.
    Restrict which AI agents can reach sensitive MCP servers by configuring Allowed Agents.
    📍In the QuilrAI console, go to Settings › AI Gateway › MCP Gateway › Allowed Agents, add a user-agent keyword for the permitted client (e.g. cursor), and select which MCP servers that agent may access.
    Enable the Policy Engine to enforce centrally published guardrail and tool rules across all servers, overriding local settings.
    📍In the QuilrAI console, go to Settings › AI Gateway › MCP Gateway, open a server, navigate to the Settings tab, and enable the Policy Engine. The Tools, Guardrails, Token Saving, and Group & User Rules sections will show Policy Engine active and follow published policies.
    • Adds the MCP Gateway, a proxy layer between AI apps (Claude, ChatGPT, Cursor, VS Code) and MCP servers that checks every tool call against tool rules and guardrails, then logs the user, agent, and result.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Adds OneMCP, a single gateway URL that serves every MCP server a user is authorized to reach, replacing per-server URLs.
    • Adds per-server Quilr gateway URL (e.g. https://mcpgateway.quilr.ai/github/mcp) that AI clients call in place of the upstream MCP server.
    • Adds Allowed Agents configuration to restrict which AI clients (matched by user-agent keyword) may connect and which MCP servers each agent can reach.
    • Adds Security Guardrails on tool arguments and tool results, with a Stopped by guardrails metric tracking refused calls, guardrail flags, failed calls, and p95 latency.

    +12 more show less

    • Adds Group & User Rules to apply per-smart-group or per-user overrides to tool access and guardrail settings.
    • Adds Input Aliases to translate mismatched tool inputs from AI clients, surfaced via a quick fixes drawer.
    • Adds Token Saving strategies to reduce token consumption on MCP tool calls.
    • Adds API to MCP capability to turn a REST APIREST APIA web API design style where clients interact with resources over HTTP using standard methods like GET, POST, and DELETE, giving cyber tools a predictable, language-agnostic way to integrate with external services. (base URL + spec) into callable MCP tools via the API MCP server type.
    • Adds Policy Engine that, when active, replaces local Tools, Guardrails, Token Saving, and Group & User Rules settings with centrally published policies.
    • Adds API Tokens for direct (non-OAuth) client connections to MCP servers.
    • Adds a User dashboard at mcpgateway.quilr.ai where end users sign in, connect accounts, and copy their gateway URLs.
    • Adds MCP Library with an install-request queue, allowing users to request servers that administrators can approve.
    • Adds per-server and workspace-level analytics including tool calls, blocked calls, guardrail flags, OneMCP share, tokens saved, success rate, p95 latency, users, and servers over time.
    • Adds Connections view showing which users have connected to which servers and their individual tool call history.
    • Adds Health tab covering gateway verdict, rejected requests, access denied, auth challenges, OAuth refreshes, upstream errors, and rejection reasons across the whole gateway.
    • New servers default to all tools turned off, so no tools are reachable until explicitly enabled.

    17 details in Everything

14. docs update Oct 8, 2026 · issue 097

    QuilrAI Workflow Agents: build, publish and run governed AI agents from console, chat, Slack or a schedule⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Navigate to Workflow Agents and create a governed AI agent that runs via Slack, with tool approvals and run limits enforced through your MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once..
    📍1. In the QuilrAI Console, go to Settings › AI Gateway › Workflow Agents. 2. Click + Create agent and set a name, System prompt, model (from Your models or a Quilr model), and select tools from your MCP connections. 3. Set Maximum turns and Run timeout (seconds) under run limits. 4. Click Connect Slack to add Slack as a trigger surface. 5. Publish the agent — it is now reachable via Slack mention or DM, Chat, Start a task, or a schedule.
    Build a Team of specialist agents under a lead model so a single Slack mention draws on multiple agents and returns one combined answer.
    📍1. In the QuilrAI Console, go to Workflow Agents. 2. Click Build a team in the header. 3. Select two to six published agents and choose a lead model to combine their answers. 4. Publish the team — it appears on My agents and can be triggered from Chat, Slack or a schedule like any single agent.
    • Adds Workflow Agents to the QuilrAI Console, letting you create, publish and run AI agents that call tools via your organization's MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. connections and LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code. Gateway.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Supports four agent trigger surfaces: the QuilrAI Console (Start a task), Chat, Slack mention or DM, and scheduled runs.
    • Agents can be assembled into a Team of two to six published agents under a lead model that combines their answers into one result.
    • Agent results are returned as Markdown, JSON or a table, posted back to the surface where the run started.
    • Supports five agent runtimes (Engine): LangGraph (recommended), OpenAI Agents SDK, Claude Agent SDK, Google ADK, and CrewAI.

    +5 more show less

    • Every tool call is governed by MCP Gateway rules and guardrails, tool approvals, and per-agent Maximum turns and Run timeout (seconds) limits.
    • Adds a Library tab of editable agent templates and a Usage tab showing runs, completion rate, tool attempts and duration per agent.
    • Adds Connect Slack on the Workflow Agents page to add Slack as a tool for agent triggers.
    • Agent versioning tracks every publish as a new Revision; runs in progress keep the version they started with and earlier revisions can be restored.
    • Access is re-checked on every run — model access, tool access and sharing — ensuring each call goes through the same gateways and logs as all other AI traffic.

    10 details in Everything

15. docs update Oct 8, 2026 · issue 097

    QuilrAI adds MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. Threat Detection — static code/dependency scans and live tool-surface enumeration with threat modelling for MCP servers.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Vet an MCP server's repository for supply-chain risks and exposed tool abuse vectors before adding it to the MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once..
    📍In the QuilrAI console, go to Assessments › Red Teaming › MCP Threat Detection. Enter a public repository URL and/or a live MCP server address, tick the acknowledgement checkbox, then click Run deep scan. Review findings under Recent scans.
    • Adds live tool-surface enumeration for a running MCP server, performing a read-only inspection of the tools the server exposes.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Accepts a public repository URL, a live MCP server, or both as inputs in a single scan run.
    • Lists completed and in-progress scans under Recent scans with findings and coverage details for review.

    3 details in Everything

16. docs update Oct 8, 2026 · issue 097

    QuilrAI MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once. gains per-server access control: restrict which AI agents, smart groups, and users can connect before any tool is listed or called.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Lock a sensitive MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. server so only a specific user can access it even when their smart group is denied — without touching any other server.
    📍In the QuilrAI console, go to Settings › AI Gateway › MCP Gateway › (server card) › Configure › General. Scroll to the Access control card. Add Contractors to Denied smart groups, add lead@example.com to Allowed users, then click Save settings in the footer.
    Block access to unapproved or legacy MCP servers tenant-wide in a single Policy Engine rule, so no caller can reach them through the gateway regardless of group membership.
    📍In the QuilrAI console, go to Govern › Policy Engine › MCP Gateway. In the Session stage, open the MCP Server Access card and add a rule: when MCP name is any of Unapproved Notes or Legacy CRM, set MCP access to deny. Publish the revision to apply.
    Restrict an MCP server so it is reachable only via OneMCP and not through a direct connection, by matching on route kind.
    📍In the QuilrAI console, go to Govern › Policy Engine › MCP Gateway. In the Session stage, open the MCP Server Access card and add a rule: when route kind is direct, set MCP access to deny. Publish the revision to apply.
    • Adds Access control card on each MCP server's Configure > General page to set Allowed agents, Allowed smart groups, Denied smart groups, Allowed users, and Denied users — denying everyone not matched before any tool is listed or called.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Adds MCP Server Access card (stage 1, Session) in Govern > Policy Engine > MCP Gateway with MCP access allow/deny effect, supporting match on MCP name, identity provideridentity providerA service that authenticates users and supplies identity information to other applications. It is the sign-in authority behind many single sign-on systems., client IP, agent name, agent classification, route kind (direct, onemcp, workflow), tags, transport, and auth type.
    • Adds OneMCP Features card in the same Session stage to toggle OneMCP dynamic tools and memory on or off per caller.
    • Access control rules apply only to MCP Gateway traffic; direct client connections to an MCP server bypass these rules.

    4 details in Everything

17. docs update Oct 8, 2026 · issue 097

    QuilrAI MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once. gains a browsable MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. Library with one-click install, user install requests, and internal-network MCP support.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Approve a teammate's request to install an MCP and gate it to shared API-key auth — useful when centralising credential management across the tenant.
    📍1. Go to Settings › AI Gateway › MCP Gateway › Library 2. Under Requested by your team, find the pending request (shows requester name and time). 3. Click Set up on that MCP row. 4. Enter the Upstream API key, set Authentication scope to Shared by the whole tenant, configure placement and prefix, then click Install. 5. Go to Settings › Tools and enable the newly installed MCP's tools.
    Register an internal MCP server hosted inside your private network so the Quilr gateway can reach it through your firewall.
    📍1. Allowlist the Quilr gateway IPs on your firewall, VPC security group, or reverse proxy. 2. Go to Settings › AI Gateway › MCP Gateway › Library. 3. Click Add MCP server and enter the internal server URL. 4. Complete sign-in configuration (API key or OAuth) and click Install.
    Set up an OAuth-backed MCP from the library using your own OAuth app registration.
    📍1. Go to Settings › AI Gateway › MCP Gateway › Library. 2. Find the MCP and click Set up. 3. Copy the displayed OAuth callback URL and register it in the provider's OAuth app. 4. Enter the OAuth client ID and OAuth client secret, then click Install. 5. Connect the MCP once as an administrator via OAuth Connect.
    • Adds an MCP Library in Settings > AI Gateway > MCP Gateway > Library for discovering and installing provider-native and Quilr-built MCP servers without manually entering URLs.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Supports filtering MCP Library entries by Status (Installed, Not installed), Type (Remote (HTTP), Local package (stdio)), and Sign-in (OAuth, API key, OAuth passthrough, No sign-in).
    • Provides per-MCP install actions: Install (no-credential MCPs), Set up (API key or custom OAuth app), Review (local packages), Continue setup, Settings, and Uninstall.
    • Enables API-key-based MCP setup via Upstream API key with configurable Authentication scope (Shared by the whole tenant or Each user brings their own), key placement, and prefix — stored at the gateway and never shown again.
    • Enables custom OAuth app setup by registering the displayed OAuth callback URL with the provider, then entering OAuth client ID and OAuth client secret.

    +4 more show less

    • Adds a user install-request workflow: users who lack install permission see Request instead of Install; pending requests appear as a count on the Library button and in the Library queue row of the Tools reachable tile, and are listed under Requested by your team with requester name and time.
    • Adds Add MCP server action for registering any MCP not in the library — by URL, REST APIREST APIA web API design style where clients interact with resources over HTTP using standard methods like GET, POST, and DELETE, giving cyber tools a predictable, language-agnostic way to integrate with external services., or local package.
    • Supports internal (private-network) MCP servers by allowlisting Quilr gateway IPs on firewalls, VPCVPCA logically isolated virtual network inside a public cloud provider's infrastructure, letting tools deploy resources with private addressing, controlled routing, and firewall rules without managing physical hardware. security groups, or reverse proxies, then adding the server with Add MCP server.
    • After installation, MCP tools are activated via Settings > Tools.

    9 details in Everything

18. docs update Oct 8, 2026 · issue 097

    QuilrAI MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once. lets admins register MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. servers, govern tool access with guardrails, and monitor agent activity via a unified gateway URL.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ USE IT
    Connect a non-OAuth AI client (e.g. a headless agent) to a specific MCP server using a bearer tokenbearer tokenA credential that grants access to whoever presents it, without a separate proof of possession. Protecting the token matters because a stolen copy can be used by someone else. and user identity header.
    json copy

    {
      "mcpServers": {
        "quilr-mcp": {
          "url": "https://mcpgateway.quilr.ai/<your-mcp-slug>/mcp",
          "headers": {
            "Authorization": "Bearer <your-token>",
            "mcpuser": "user@company.com"
          }
        }
      }
    }

    Register a remote MCP server through the gateway admin UI so agents can discover and call its tools.
    📍In the QuilrAI console, go to Settings › AI Gateway › MCP Gateway, click Add MCP server, choose Remote server, enter its Transport URL, then click Probe and continue.
    Enable only specific tools on a registered server and add guardrails to block sensitive data before agents can call it.
    📍In the QuilrAI console, go to Settings › AI Gateway › MCP Gateway, open the server card, go to the Tools section and toggle on the tools agents may call, then go to Guardrails to configure block or redact rules, and click Save settings.
    • Adds Settings > AI Gateway > MCP Gateway to register MCP servers — supporting ready-made library integrations, remote servers (via Transport URL), REST APIsREST APIsA convention for building HTTP-based interfaces where resources are addressed by URL and manipulated with standard verbs like GET, POST, and DELETE, giving cyber tools a predictable way to integrate with external services. converted to MCP, and local CLI packages.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Adds https://mcpgateway.quilr.ai/quilrone/mcp as a OneMCP endpoint that exposes every permitted MCP server to an AI agent through a single connection.
    • Adds per-server https://mcpgateway.quilr.ai/<slug>/mcp gateway URLs for single-server connections, with Authorization: Bearer <token> and mcpuser header support for non-OAuth clients.
    • Adds Settings > API tokens on each server card to generate tokens for clients that cannot use OAuth.
    • Adds tool-level access control per server: enable individual tools agents may call, apply guardrails to block or redact sensitive data in tool arguments and results, restrict servers to specific agents, smart groups, or users via General > Access control, and set per-group or per-user rules via Group & User Rules.

    +3 more show less

    • Adds Token saving setting per server to compress tool results.
    • Adds monitoring views — Overall analytics, per-server Logs, and tabs for Analytics (tool calls, blocked calls, guardrail flags, tokens saved), Activity (per-call user/agent/guardrail detail), and Health (rejected requests, access denials, upstream errors).
    • Adds a Log Export API to forward gateway logs to an external data platform.

    8 details in Everything

19. docs update Oct 8, 2026 · issue 097

    QuilrAI adds Costs & Savings dashboard with spend, token usage, savings techniques, attribution, and provider health across AI sources.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Identify which team or app is driving the most AI spend in the current period so you can prioritise cost-reduction conversations.
    📍In the console, go to Observe › Costs & Savings › Attribution. Set the metric to Spend, then group by Departments (or Applications). Use the Filters control to narrow to a specific source if needed.
    Find savings techniques that are not yet enabled and navigate directly to their configuration to switch them on.
    📍In the console, go to Observe › Costs & Savings › Savings. Review the ranked Settings to change list, then click Open settings next to any technique (e.g. text compression or smart JSON) to jump straight to its configuration page.
    Spot provider-side failures that may be costing you traffic — useful when users report intermittent AI errors.
    📍In the console, go to Observe › Costs & Savings › Providers. Review the failed-request share and estimated blocked requests for each provider, and check the Last incident column for recent outages.
    • New Observe > Costs & Savings section shows organisation-wide AI spend in USD, token usage, and tokens already saved, broken down by source (LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code. Gateway, MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once., Endpoint Agent, Browser Extension, and connected platforms).⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Attribution tab lets teams group usage metrics (Spend, Tokens, or Tokens saved) by Applications, Users, Models, Tools, Agents, Departments, Smart Groups, Sources, Usage types, Topics, or Repositories — with per-entry daily sparklines and period-over-period spend change.
    • Savings tab surfaces a ranked Settings to change list of token-saving opportunities, each with an Open settings deep-link to the relevant configuration page.
    • Savings tab shows per-technique breakdown: LLM Gateway techniques include provider prompt cache, text compression, markdown-to-text, and smart JSON; MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. Gateway techniques include smart JSON and HTML-to-text; Endpoint Agent contributes Claude compression.
    • Per-app saving controls matrix shows which saving technique is on or off for each application.

    +2 more show less

    • Habits tab surfaces usage patterns (Long sessions, Session reuse after idle, Single sessions) with per-person impact, evidence, and token counts — designed for coaching workflows.
    • Providers tab shows per-provider key health, request outcomes, failed-request share, estimated blocked requests, last incident, and live status to diagnose rate limits, auth failures, and provider errors.

    7 details in Everything

20. docs update Oct 8, 2026 · issue 097

    QuilrAI LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code. Gateway gains security guardrails covering PIIPIIPersonally Identifiable Information — data that can identify a specific individual, such as names, email addresses, or SSNs. Cyber tools flag or redact it to limit exposure and meet privacy regulations./PHIPHIProtected Health Information: individually identifiable health data regulated under the US HIPAA law, whose strict handling requirements drive cyber tools to audit, mask, or restrict access to it./PCIPCIA hardware bus standard that connects peripheral devices to a computer's motherboard; cyber tools targeting hardware-level forensics or memory analysis use it to access device registers and DMA regions directly. redaction, adversarial-prompt detection, hallucination checks, and source-IP allowlisting.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Enable guardrails for a new app and switch Auth & Secrets from Monitor to Block before going to production, so API keys and passwords in prompts are rejected rather than just logged.
    📍In the QuilrAI console: Settings › AI Gateway › LLM Gateway › <your app> › Configure › Guardrails. Under Data risks, find Auth & Secrets, set Action to Block, then save.
    Use the Policy Engine to redact Aadhaar numbers and block secrets for a support copilot app while only monitoring names — scoping rules to the specific application rather than globally.
    📍In the QuilrAI console: Policy Engine › LLM Gateway › Data & Adversarial Risks card. Add rule 1: When Application is Support Copilot and data found is Aadhaar Number / VID, then action Redact, Risk level High. Add rule 2: When Application is Support Copilot and data found is Name, then action Monitor. Add rule 3: When Application is Support Copilot and data found is Auth & Secrets, then action Block, Risk level Critical. Publish revision.
    Restrict your LLM Gateway app to only accept calls from your corporate network CIDRCIDRA notation for a block of IP addresses, such as 10.0.0.0/24. The number after the slash says how many leading address bits identify the network., rejecting all other source IPs at the gateway level.
    📍In the QuilrAI console: Settings › AI Gateway › LLM Gateway › <your app> › Configure › Guardrails › Source IP restrictions. Toggle Enabled on and enter 10.0.0.0/8, 203.0.113.10 in Allowed source IPs, then save.
    • Adds Guardrails tab in the app's Configure section with six parts: Default action, Data risks, Adversarial risks, Precision detections, Hallucination check, and Source IP restrictions.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Adds Adversarial risks guardrail with 13 categories including Prompt Injection Techniques, Jailbreak Techniques, Prompt Context Corruption, Semantic Adversarial Prompts, Social Engineering Prompts, System, Guardrail & Security Disclosure, Security Exploit & Payload Enablement, Cybersecurity Frameworks & Standards Mention, Response Risks, Hateful or Offensive Content, Violence & Harmful Content, Fraudulent or Illegal Activity Content, and Malicious Scripts (opt-in); each supports Block or Monitor.
    • Adds Precision detections with a built-in catalog of more than 80 exact-match identifiers (SSN, Aadhaar, IBAN, IMEI, PUK code, and others), each independently switchable with its own action (Redact, Partial redact, Block, or Monitor; default Monitor).
    • Adds Hallucination check that flags likely-fabricated responses at a configurable risk level (Low/Medium/High; default Medium) with Block or Monitor action; runs on non-streaming responses only.
    • Adds Source IP restrictions guardrail that accepts gateway calls only from comma-separated IPv4, IPv6IPv6The current version of the Internet Protocol, standardized by the IETF, using 128-bit addresses to replace the exhausted IPv4 space. Cyber tools target it because modern networks run dual-stack or IPv6-only., or CIDR values entered in Allowed source IPs; calls from unlisted addresses are rejected.

    +7 more show less

    • Adds Custom Detection option to match user-defined identifiers via regex when built-in precision detections are insufficient.
    • Adds Action mix panel that counts how many of the 20 categories (7 data + 13 adversarial) use each action or are off.
    • Adds Scan tool-call arguments setting in the Policy Engine's Data & Adversarial Risks card to evaluate each tool call's arguments independently (supports Monitor and Block only).
    • Adds Policy Engine Data & Adversarial Risks card for LLM Gateway, supporting scope by Everyone, People, Smart group, Application, or App tag; model/provider/API surface/environment targeting; per-type findings thresholds (at least N); and language blocking.
    • Extends guardrail endpoint coverage to Chat completions (including Bedrock Converse, Vertex Gemini, Anthropic via translation), Native Anthropic Messages, OpenAI Responses, Bedrock Runtime (boto3), Native Vertex / Gemini generateContent, embeddingsembeddingsLists of numbers a model produces to represent the meaning of text, images or code, so similar items sit close together. They power semantic search and RAG., speech-to-text/text-to-speech, Sarvam speech synthesis/transcription/translation/transliteration/language detection, and Copilot Studio; OpenAI Realtime (websocket) is logged but not scanned.
    • New apps start with all 6 data-risk categories on and 12 of 13 adversarial categories on (Malicious Scripts is off), default action Monitor, and precision detections/hallucination check/source IP restrictions off.
    • Action resolution follows: category action → app default action → Monitor; Set everything to default clears per-category actions so all categories follow the app default.

    12 details in Everything

21. docs update Oct 8, 2026 · issue 097

    QuilrAI LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code. Gateway V2 adds tenant-wide platform providers, a hosted model catalog at https://models.quilrai.dev/v1, scoped model API keys, and a browser Playground.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ TRY IT
    Call a QuilrAI-hosted model directly from the CLI without provisioning your own provider account — useful for quick triage or ad-hoc analysis.
    copy

    $ curl 'https://models.quilrai.dev/v1/chat/completions' \
      -H "Authorization: Bearer $QUILR_MODEL_API_KEY" \
      -H "Content-Type: application/json" \
      --data '{"model": "deepseek/deepseek-v3.2", "messages": [{"role": "user", "content": "Explain zero trust in one sentence."}], "max_tokens": 1024}'

    Route QuilrAI-hosted models through a gateway app to apply guardrails and logging — configure the general provider pointing at the hosted catalog.
    yaml copy

    base_url: https://models.quilrai.dev/v1
    api_key: sk-quilrllm-...
    models:
      - deepseek/deepseek-v3.2

    Create a scoped model API key and link it to specific allowed models so automated pipelines cannot call models outside the approved set.
    📍Go to Settings › AI Gateway › Models › API keys, click Create API key, enter a Key name (1–128 characters), select one or more Allowed models from the catalog, then click Create API key and copy the displayed sk-quilrllm-... key — it is shown only once.
    • Adds POST /chat/completions at https://models.quilrai.dev/v1 — an OpenAI-compatible endpoint for calling QuilrAI-hosted models directly with a sk-quilrllm-... model API key and Authorization: Bearer $QUILR_MODEL_API_KEY.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Adds model API keys (sk-quilrllm-...) scoped to specific allowed models from the hosted catalog, created at Settings > AI Gateway > Models > API keys; keys have no expiry, are recorded in the audit log, and are capped only by organization credit.
    • Adds RBACRBACRole-based access control, where permissions are assigned to roles such as reader or administrator and users receive those roles. It makes access easier to manage than individual permission lists. V2 permissions llm.apps.create, secrets.reveal, and llm.apps.delete controlling model API key creation and revocation.
    • Introduces platform providers — tenant-wide provider credentials configured once at Settings > AI Gateway > Models or via Provider configuration on the Settings > AI Gateway > LLM Gateway page and shared across any number of apps, with changes (disable, delete, credential update) propagating to every linked app.
    • Adds support for gateway provider types openai, openai_responses, openai_assistants, openai_realtime (OpenAI); anthropic_messages, anthropic (Anthropic); azureopenai, openai_responses_azure, openai_assistants_azure, openai_realtime_azure, anthropic_messages_azure (Azure); bedrock, anthropic_messages_bedrock, bedrock_embeddings, bedrock_rerank (Amazon Bedrock); vertex_ai, gemini_chatcompletions (Google); deepseek (DeepSeek); sarvam (Sarvam); cohere_rerank, jina_rerank, voyage_rerank (Cohere/Jina/Voyage); general, general_rerank (Custom endpoint).

    +5 more show less

    • Adds a Models page (Settings > AI Gateway > Models) with four tabs — Models, API keys, Usage, and Playground — for managing the full model inventory, keys, credit spend, and live chat testing.
    • Adds a QuilrAI provided models catalog view at Settings > AI Gateway > Models > Models, searchable by model ID or provider prefix, sortable by input/cached-input/output price, with per-row Chat access to the Playground.
    • Adds a browser Playground at Settings > AI Gateway > Models > Playground supporting temperature (0–2), Top P (0–1), max output tokens (1–4,096), system and user prompts up to 16,000 characters each, and conversations up to 20 messages.
    • Adds Usage tab at Settings > AI Gateway > Models showing remaining credit, spend, requests, tokens, and per-model throughput for QuilrAI-provided models.
    • Enables routing QuilrAI-provided models through an LLM Gateway app by adding a general (Custom endpoint / Chat completions) provider with base_url set to https://models.quilrai.dev/v1 and api_key set to a sk-quilrllm-... model API key, applying the app's guardrails, routing, limits, and logs.

    10 details in Everything

22. docs update Oct 8, 2026 · issue 097

    QuilrAI LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code. Gateway lets teams create apps that proxy LLM traffic through a regional gateway URL, with multi-provider failover, per-app or shared platform credentials, and a Policy Engine for centrally enforced guardrails.

    • Swap base_url to guardrails-usa-2.quilr.ai/openai_compatible/ and set api_key to your Quilr key to route any OpenAI-compatible SDK through the gateway without other code changes.
    • Create an LLM Gateway app via Settings > AI Gateway > LLM Gateway > Create App, choosing between Platform providers (shared team credentials, V2 console only) or App-only credentials (per-app isolation).
    • Connect apps to QuilrAI-provided models by adding a Custom endpoint provider with the Chat completions API, base URL https://models.quilrai.dev/v1, and a model API key from Settings > AI Gateway > Models > API keys.
    • Configure per-app Security Guardrails, Guardian Agent, Custom Detections, Rate and Token Limits, Routing, Token Saving, Prompt Store, Identity Aware, and Self-Service settings via the app card's Configure buttons.
    • Enable a Policy Engine to override app-level guardrails, limits, routing, identity requirements, and Prompt Store enforcement with centrally published policies.

    +3 more show less

    • Inspect each request's model, provider, guardrail outcome, HTTP status, tokens, and latency in Activity > Requests via the app card's Logs view.
    • Order providers for failover by ticking them under Available in the app wizard; the first ticked becomes Primary, and the gateway automatically fails over to the next enabled provider in order when the primary fails.
    • Set Quilr key name and Initial key expiry under Key settings in the Create App wizard; leave expiry blank for a key that never expires.

    8 details in Everything

    • New detection Default data-risk guardrail categories
    • New detection Default adversarial-risk guardrail checks
23. docs update Oct 8, 2026 · issue 097

    QuilrAI Policy Engine docs reveal a unified card-based enforcement layer spanning Browser Extension, LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code. Gateway, MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once., and Endpoint Agent.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Test what policy wins for a contractor calling a specific MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. tool before pushing a new revision live.
    📍In the QuilrAI Console, go to Govern › Policy Engine › MCP Gateway tab, then click Describe a call. Fill in the user (a member of the Contractors smart group), the MCP server, and the tool name. Every card collapses to the value that would win from the current shared draft — confirm onemcp.dynamic_tools is false and cache.mode is as expected before clicking Publish.
    Lock down contractor sessions with a single QuilrQL policy combining OneMCP, memory, claims, and cache controls at the session stage.
    📍contractor_session_posture session runs on session · priority 500 When Smart groups includes (ignoring case) Contractors Then OneMCP dynamic tools → false OneMCP memory → deny forward user claims → false cache mode → none
    Migrate an Endpoint Agent deployment from Basic policies to the card-based Policy Engine without recreating existing per-app rules.
    📍In the QuilrAI Console, go to Govern › Policy Engine › Endpoint Agent tab, then click Convert to Policy Engine. Review the Detection configurations and Application configuration sub-tabs for ChatGPT, Claude, Cursor, Copilot, Gemini, DeepSeek, Ollama, and LM Studio cards before publishing.
    • Adds Describe a request button (LLM Gateway tab) and Describe a call button (MCP Gateway tab) to preview which policy wins for a given request before publishing.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Introduces QuilrQL policy language with a source view for reviewing, diffing, or bulk-editing policies as exact text.
    • Surfaces block_request_secrets example policy showing named fields: name, priority, stage, conditions, and effects — with priority 500 as the default for new policies.
    • MCP Gateway advanced policies expose effects quota.minute, quota.hour, quota.day, quota.window, quota.timezone, quota.dimensions, quota.id, concurrency.limit, concurrency.ttl_seconds, and concurrency.dimensions at the request stage.
    • MCP Gateway cache.mode effect (values: shared, tenant, or none) is available at the session stage under Capability Cache & Isolation.

    +10 more show less

    • Web Search Security card exposes web_search.zia_timeout_seconds, excluded_domains, url_overrides, and result_domain_action effects at the response stage.
    • Token Savings card exposes token_saving.smart_json_compression, html_to_text, markdown_to_text, and text_compression effects at the response stage.
    • MCP Gateway evaluates policy cards in four ordered stages: 1 Session (once per connection), 2 Discovery (per listed capability), 3 Request (before dispatch), and 4 Response (before result returns).
    • Adds Convert to Policy Engine button on the Endpoint Agent tab to migrate from Basic policies mode to the card-based engine.
    • LLM Gateway Policy Engine controls the following app settings sections when enabled: Security Guardrails, Hallucination Protection, Identity & Network Trust, Guardian Agent, Rate/Token/Timeout Limits, Token Savings, Routing Groups & Fallbacks, Prompt Store — each showing Controlled by Policy Engine with View policies and Edit anyway options.
    • Policy Engine introduces a versioned draft-and-publish workflow: all card edits accumulate in a shared draft, publishing creates a numbered revision (Revision N), and rollback republishes any earlier revision without altering classic settings.
    • Introduces conflict-resolution rules: restriction wins over permission, priority settles single-value conflicts, risk level only climbs, and redact/partial-redact apply per data type without competing across types.
    • Browser Extension controls table supports filtering by Posture (AI Risks, Data Risks, Device Risks, IT Support, MFAMFAMulti-factor authentication, requiring proof from different categories, such as a password and a hardware key, to sign in. It reduces reliance on a password alone. Risks, Password Hygiene) and Type, sorting by Recently modified, plus Export and Add control actions.
    • Browser Extension control edit form exposes criticality (Very Low to Very High), mode (Monitor or Action), When this happens conditions, and Perform action configuration.
    • MCP Gateway tool-matching fields include read_only, destructive, idempotent, and open_world annotations on the Tool condition group.

    15 details in Everything

24. docs update Oct 8, 2026 · issue 097

    QuilrAI adds an Observe > Overview dashboard with Adoption, Agentic Estate, and Posture & Risk tabs for AI governance at a glance.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Run a weekly risk review: see what was flagged, how it was handled, and which assets need immediate attention.
    📍In the console, go to Observe › Overview, select the Posture & Risk tab, set the period to Last 7 days, then review Risk categories and How findings were handled. Click any finding category to drill into Findings & Interactions.
    Find MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. servers bypassing the gateway and work through the remediation queue one by one.
    📍In the console, go to Observe › Overview › Agentic Estate tab. Under Control coverage, filter MCP servers by route Outside gateway. Use the Act now panel to open the flagged asset via Review asset, then advance with Next in queue.
    Spot dormant licences and newly appeared unapproved AI apps across departments for an adoption report.
    📍In the console, go to Observe › Overview › Adoption tab, set the period selector to the desired range. Review Who's using AI for Dormant users, then inspect Across the organization flow chart filtered by New this period to identify new unapproved apps.
    • Adds the Observe > Overview dashboard with three tabs — Adoption, Agentic Estate, and Posture & Risk — and a period selector (e.g.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠ Last 7 days) that applies across all tabs.
    • Adds the Adoption tab, showing headline tiles for People using AI, AI interactions, Power users, and AI applications, plus breakdowns by usage mode (Chat assistants, Coding agents, MCP agents, API & service accounts, Other) and a department/team/people flow chart marking sanctioned vs. unapproved apps.
    • Adds the Agentic Estate tab, surfacing Agentic assets, Shadow AI, share of MCP servers through gateway, and High-risk assets, with an Act now queue and per-platform panels for OpenAI, Microsoft Copilot Studio, and Azure AI Foundry.
    • Adds the Posture & Risk tab, displaying findings by category (PIIPIIPersonally Identifiable Information — data that can identify a specific individual, such as names, email addresses, or SSNs. Cyber tools flag or redact it to limit exposure and meet privacy regulations., adversarial prompts, auth and secrets, financial and health data) and handling outcome (Monitored, Justified, Redacted, Blocked, Failed), plus an Estate today view of ungoverned or high/critical-risk assets.
    • Adds Control coverage within Agentic Estate, categorising direct AI apps by approval status (Sanctioned, Unapproved, Needs review, Posture unknown) and MCP servers by route (Outside gateway, Both, Gateway only).

    5 details in Everything

25. docs update Oct 8, 2026 · issue 097

    QuilrAI adds integrations hub pulling AI usage from Claude, OpenAI, Copilot, Azure AI, GitHub, and TrueFoundry, with SIEMSIEMSecurity information and event management, collecting logs from many systems and correlating events to find suspicious activity. Analysts use it to investigate alerts and incidents./syslog/webhook forwarding.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Navigate to the integrations hub to connect a new AI platform source or SIEM destination.
    📍Console › Settings › Integrations
    • Adds Console › Settings › Integrations hub to browse, install, and manage integrations by capability.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Pulls AI usage and inventory from Claude.ai via the Anthropic Compliance API integration.
    • Pulls AI usage and inventory via the OpenAI Compliance integration.
    • Pulls AI usage and inventory from Microsoft Copilot Studio.
    • Pulls AI usage and inventory from Azure AI Foundry.

    +7 more show less

    • Pulls AI usage and inventory from GitHub.
    • Pulls AI usage and inventory from TrueFoundry.
    • Forwards findings and audit events to syslog.
    • Sends findings and audit events to a user-defined endpoint via webhook.
    • Forwards findings and alerts to Microsoft Sentinel.
    • Forwards findings and alerts to Splunk, Datadog, and Slack.
    • Adds SAMLSAMLA standard for passing signed identity assertions between a sign-in provider and an application. It lets a user sign in through their organization's identity provider. and Okta sign-in integration.

    12 details in Everything

26. docs update Oct 8, 2026 · issue 097

    QuilrAI Endpoint Agent adds per-device AI governance: monitors desktop chat apps, coding agents, local models, and MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. servers.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Navigate to the Endpoint Agent in the QuilrAI console to begin discovering and governing AI apps running on company devices.
    📍Console › Govern › Policy Engine › Endpoint Agent
    • Adds the Endpoint Agent, accessible via Console › Govern › Policy Engine › Endpoint Agent, to discover and govern AI running on company devices — including desktop chat apps, coding agents, local models, and MCP servers.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Provides per-app monitoring and guardrails through App Policies, letting teams set rules on individual AI applications detected on endpoints.
    • Introduces an Agent Kill Switch to disable the endpoint agent per device or tenant-wide without a full redeploy.

    3 details in Everything

27. docs update Oct 8, 2026 · issue 097

    QuilrAI adds a browser extension to monitor AI app usage, block sensitive data at the prompt, and guide users in real time.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Configure and validate the browser extension after deploying it across your fleet via Intune.
    📍Console › Settings › Browser Extension
    • Adds a browser extension that surfaces which AI apps employees use, intercepts sensitive data before it reaches AI prompts, and delivers in-context guidance to end users.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Supports deployment via Microsoft Intune, Jamf Pro, and Group Policy for enterprise-wide rollout.
    • Introduces browser controls to monitor or act when a defined use case occurs in the browser.
    • Introduces action requests, allowing administrators to approve or reject user justifications for blocked actions.
    • Adds clipboard monitoring to detect sensitive content copied into AI prompts.

    +3 more show less

    • Adds file indexing as a browser-extension capability.
    • Adds an agent kill switch to halt AI agent activity from the browser extension.
    • Allows scope configuration to monitor all domains or work domains only.

    8 details in Everything

28. docs update Oct 8, 2026 · issue 097

    QuilrAI adds Red Teaming suite covering gateway apps, MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. servers, agents, and multi-model comparison via console and API.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Red team an HTTP agent endpoint to surface attack pathsattack pathsA possible sequence of steps an attacker could take to reach a valuable target. It connects weaknesses, permissions and reachable systems rather than treating each finding separately. before promoting to production.
    📍In the console, go to Console › Assessments › Red Teaming, select Red team an agent, enter your HTTP agent endpoint URL, choose attacks from the Attack library, and run the assessment.
    Compare resilience across multiple models by running identical attack suites against 2–8 candidates in a single session.
    📍In the console, go to Console › Assessments › Red Teaming, select Compare models, add between 2 and 8 model targets, then launch the shared attack suite to view side-by-side findings.
    Scan an MCP server for static, dependency, and live tool-surface vulnerabilities before connecting it to an agent.
    📍In the console, go to Console › Assessments › Red Teaming, select Vet an MCP server, provide the server target, and run the MCP Threat Detection scan to review static and live findings.
    • Adds Console › Assessments › Red Teaming to run attack suites against gateway apps through their configured model.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Adds MCP Threat Detection with static, dependency, and live tool-surface scanning of MCP servers.
    • Adds agentic red teaming against any HTTP, WebSocketWebSocketA protocol that upgrades an HTTP connection into a persistent, full-duplex channel, letting a server push data to a client without repeated polling. Cyber tools use it for real-time event streaming and live command output., or voice agent endpoint.
    • Adds model red teaming to run the same attack suite across 2 to 8 models simultaneously for side-by-side comparison.
    • Adds LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code. Intelligence Assessment capability for structured evaluation of model behaviour under attack.

    +3 more show less

    • Adds ability to turn red-team findings directly into detections and hardened prompts.
    • Adds runs, findings, and schedules management for Red Teaming assessments.
    • Adds an Attack library for browsing and selecting real attack techniques used in assessments.

    8 details in Everything

29. docs update Oct 8, 2026 · issue 097

    QuilrAI: Adds Workflow Agents accessible via Console › Settings › AI Gateway › Workflow Agents, routing every model and tool call through your existing gateways.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Connect a Slack workspace to an agent so team members can trigger and chat with it directly from Slack.
    📍In the console, go to Console › Settings › AI Gateway › Workflow Agents, open your agent, then navigate to Slack options and connect a Socket Mode Slack app.
    Set up a recurring scheduled run so an agent (e.g. a research or code review agent) runs automatically without manual triggers.
    📍In the console, go to Console › Settings › AI Gateway › Workflow Agents, open your agent, then under Share and manage configure a recurring schedule and assign groups or approvals as needed.
    • Supports running agents from the console, chat, Slack (via Socket Mode Slack app), or on a schedule with recurring runs.
    • Enables sharing agents with teams via groups, approvals, and versioning.
    • Provides library templates for common use cases including research, support, and code review.

    3 details in Everything

30. docs update Oct 8, 2026 · issue 097

    QuilrAI MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once. debuts as a control plane for AI agents: OneMCP single-endpoint aggregation, tool-level ACLsACLsAn access control list, a set of rules allowing or denying operations for particular users, services or network addresses. It decides who can use a resource., human approval, tool change watch, security guardrails, Local MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface., and a Log export API.

    • Introduces MCP Gateway, a managed control plane that lets teams register MCP servers once and enforce who can call which tool across AI agents such as Claude, Cursor, and ChatGPT.
    • Adds OneMCP, a single endpoint with dynamic tool callingtool callingA model's ability to answer with a structured request to run a named function or API, which the application executes and returns. It is how agents take actions. that aggregates multiple registered MCP servers behind one connection.
    • Supports turning any API into a set of MCP tools via the MCP Library.
    • Supports OAuth connect and OAuth permissions flows for allowing specific agents to authenticate with connected MCP servers.
    • Adds API token support for allowing agents to authenticate with connected MCP servers.

    +7 more show less

    • Adds server-level and group/user access-control rules to restrict which agents can reach which MCP servers and tools.
    • Adds tool visibility controls to show or hide specific tools from specific agents or groups.
    • Adds human approval workflow for tool calls that require a human to sign off before execution.
    • Adds tool change watch to alert administrators when a registered MCP server's tool surface changes.
    • Adds security guardrails including web search security, claims forwarding, input aliases, usage quotas, concurrency limits, and token saving.
    • Introduces Local MCP, allowing locally running MCP servers to be registered, packaged, and connected to AI apps through the gateway.
    • Exposes a Log export API for exporting every tool call made through the gateway.

    12 details in Everything

    • New integration Quilr-provided managed MCP integrations
    • New integration Provider setup guides for third-party MCP integrations
31. docs update Oct 8, 2026 · issue 097

    QuilrAI launches with browser extension, endpoint agent, LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code. gateway, MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. gateway, red teaming, and open-source AgentGuard for enterprise AI governance.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Route a service's model calls through the LLM Gateway to enforce guardrails and track costs across all providers from one endpoint.
    📍In the Console, go to LLM Gateway › Quick start, then LLM Gateway › Providers and models to configure your providers (OpenAI, Anthropic, Azure, Bedrock, Vertex), and LLM Gateway › Security guardrails to enable data-loss and content policies.
    Proactively find prompt-injectionprompt-injectionAn attack where text the model reads, such as a web page, file or tool result, carries instructions that override the user's. It is the main security risk for agents with tools. and jailbreak exposures in a gateway app or agent before attackers do, using the built-in Red Teaming scheduler.
    📍In the Console, go to Red Teaming, select your target (gateway app, agent, model, or MCP server), and configure an on-demand or scheduled attack run to surface findings as detections.
    • Adds LLM Gateway — a single endpoint routing traffic to OpenAI, Anthropic, Azure, Bedrock, and Vertex with security guardrails, cost tracking, and provider/model management.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Adds MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once. — governed tool access for AI agents, connecting any MCP client or OneMCP to 400+ servers including GitHub, Slack, and Salesforce.
    • Adds Red Teaming — attacks gateway apps, agents, models, and MCP servers on demand or on a schedule, and converts findings into detections.
    • Adds Triage center (reachable at Observe › Triage center in the Console) for reviewing AI governance findings.
    • Adds Browser Extension for AI governance across web-based AI apps (ChatGPT, Claude, Gemini, Copilot) in personal and enterprise accounts.

    +6 more show less

    • Adds Endpoint Agent for governing desktop AI apps, coding agents (Cursor, Claude Code, Ollama), and local MCP servers.
    • Adds integrations pulling from Claude and OpenAI compliance APIs, Copilot Studio, Azure AI Foundry, and GitHub, and sending to Syslog, webhooks, and Microsoft Sentinel.
    • Adds India data types to LLM Gateway guardrails for localized sensitive-data detection.
    • Adds AgentGuard open-source desktop app (macOS Apple silicon, Ubuntu x86_64) that blocks secrets and PIIPIIPersonally Identifiable Information — data that can identify a specific individual, such as names, email addresses, or SSNs. Cyber tools flag or redact it to limit exposure and meet privacy regulations. from leaving in prompts and tool calls, stops installs of compromised or vulnerable packages, and compresses file reads and shell output — no proxy or cloud relay required.
    • Adds Workflow Agents support for building and sharing AI workflow agents with team and Slack integration.
    • Adds MCP Library inside the MCP Gateway for discovering and connecting governed MCP servers.
    • Adds LLM Gateway — a single endpoint routing traffic to OpenAI, Anthropic, Azure, Bedrock, and Vertex with security guardrails, cost tracking, and provider/model management.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Adds MCP GatewayMCP GatewayA server that sits between MCP clients and one or more Model Context Protocol backends, routing tool calls and aggregating context sources so a single endpoint exposes many AI-agent integrations at once. — governed tool access for AI agents, connecting any MCP client or OneMCP to 400+ servers including GitHub, Slack, and Salesforce.
    • Adds Red Teaming — attacks gateway apps, agents, models, and MCP servers on demand or on a schedule, and converts findings into detections.
    • Adds Triage center (reachable at Observe › Triage center in the Console) for reviewing AI governance findings.
    • Adds Browser Extension for AI governance across web-based AI apps (ChatGPT, Claude, Gemini, Copilot) in personal and enterprise accounts.

    +5 more show less

    • Adds Endpoint Agent for governing desktop AI apps, coding agents (Cursor, Claude Code, Ollama), and local MCP servers.
    • Adds integrations pulling from Claude and OpenAI compliance APIs, Copilot Studio, Azure AI Foundry, and GitHub, and sending to Syslog, webhooks, and Microsoft Sentinel.
    • Adds AgentGuard open-source desktop app (macOS Apple silicon, Ubuntu x86_64) that blocks secrets and PIIPIIPersonally Identifiable Information — data that can identify a specific individual, such as names, email addresses, or SSNs. Cyber tools flag or redact it to limit exposure and meet privacy regulations. from leaving in prompts and tool calls, stops installs of compromised or vulnerable packages, and compresses file reads and shell output — no proxy or cloud relay required.
    • Adds Workflow Agents support for building and sharing AI workflow agents with team and Slack integration.
    • Adds MCP Library inside the MCP Gateway for discovering and connecting governed MCP servers.

    1 more in Everything

32. docs update Oct 4, 2026 · issue 094

    QuilrAI LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code. Gateway adds request-side token-saving strategies — Smart JSON compression, HTML-to-text, Markdown-to-text, and text compression.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠

    └──▷ HOW TO FIND IT
    Enable Smart JSON compression and HTML-to-text for an app that sends tool results and web-scraped HTML, to reduce provider token costs with no code changes.
    📍Settings › Token Saving (under Optimization & policy) › turn on Smart JSON compression and HTML to text
    • Adds Smart JSON compression strategy that compacts eligible JSON objects and arrays, converting to TOON where it saves more tokens — up to ~20% savings — best for tool results and structured data.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠
    • Adds HTML to text strategy that strips HTML markup down to readable text before requests reach the provider.
    • Adds Markdown to text strategy that strips Markdown syntax that costs tokens without adding meaning.
    • Adds Text compression strategy that removes low-value prose and separator noise from long text while preserving meaning, leaving structured-looking lines untouched.
    • Surfaces token-saving metrics in the app's Analytics tab and per-request in Activity > Requests.

    +1 more show less

    • Enables token-saving strategies through Settings > Token Saving (under Optimization & policy) in the app UI; when the Policy Engine is on, the Token Savings card applies instead.

    6 details in Everything

33. docs update Oct 4, 2026 · issue 094

    QuilrAI introduces Salesforce connectivity via two MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. server paths, selectable by agent use case.

    • New integrationSupports connecting Salesforce to QuilrAI through two MCP server options, selectable based on agent requirements.

    1 detail in Everything