Comparison · agent governance · QuilrAI

QuilrAI, measured.

QuilrAI governs agents from the call path: an LLM gateway, an MCP gateway and an agent on the device. This page scores that architecture across 16 capabilities against the same capabilities in Agen.co, with the sources, the scoring ladder and the rows QuilrAI wins all on the page.

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The short answer

  • QuilrAI decides inline. Every LLM call and MCP tool call routed through its gateways gets a verdict, and a single tool call can be denied while the agent keeps working. Blocking a single action scores level with Agen.
  • QuilrAI publishes its latency two ways. The gateway documentation puts overhead at about 40ms for a typical 12,000-token request, plus roughly 700ms when the Guardian checks are on, while its AWS Marketplace listing claims sub-30ms decisions. The table scores the documented figures.
  • The agent is recognised by a User-Agent keyword or a scoped token rather than held as an identity object, and the documented agent record names no owner.
  • The human behind a call is whoever the caller passes in a header or a verified JWT, and identity is optional until enforcement is turned on. Agen resolves every action to a named accountable human, autonomous agents included.
  • Browser and endpoint sensors are sold as separate contract dimensions with a 100-license minimum, and gateway usage is metered on top. Agen is licensed per governed agent, in one console.
  • QuilrAI is stronger in the browser, at the network layer on the device, and in partner reach. Single-action blocking, the audit trail, MCP tool governance and time to first governed agent score level with ours.
The long read

What QuilrAI actually governs.

QuilrAI puts itself in front of the calls an agent makes and decides there. That is a real runtime architecture, and it is closer to ours than most. The difference is in who the decision is about.

The three enforcement points

The product is a decision engine with three ways into the traffic, and each one is worth scoring on its own.

  • LLM gatewayan OpenAI-compatible endpoint that an application reaches by changing one base URL. Requests pass through identity, rate limits, guardrails, custom detections, routing and token compression, and responses are scanned on the way back.
  • MCP gatewayone URL that multiplexes hundreds of MCP servers. Policy runs at session, discovery, request and response, so a destructive tool can be hidden from the agent, refused if called anyway, or paused until a person approves it.
  • Endpoint agent and browser extensiona daemon on macOS and Windows that hooks AI tool processes at the operating-system level, plus a browser extension that scans prompts and pastes. Together they reach coding agents and chat tools that never touch a gateway.

Above all three sits the Guardian: a per-agent policy written from a one-sentence purpose statement and a short round of clarifying questions, with a red-team agent testing it before and after it goes live.

Where QuilrAI is strongest

Three of the sixteen rows below go to QuilrAI and four are level. They follow from where it chose to sit: on the device and in the call path, with a partner channel built around both.

  • Browser and device-level network enforcementa force-installed browser extension paired with a native browser agent, rolled out through Group Policy, Intune or Jamf, and TLS inspection on managed laptops. We govern the action rather than the traffic, and run alongside whatever already inspects that traffic.
  • Speed to a first governed callone base URL change, no SDK, and first agents surfaced within minutes. Level with ours.
  • Partner reacha cloud-marketplace listing, resellers, global systems integrators and managed security providers carry the product, which gives it a wider distribution footprint than ours today.

Inline is not the same as accountable

On runtime, the two architectures are close. A tool call through the MCP gateway is judged before it lands, one call can be refused without stopping the agent, and every call is logged. The table scores those rows level or one point apart, and that is the honest reading.

The gap opens on the question a verdict is supposed to answer: who is this agent, and who answers for it? In QuilrAI the agent is recognised by a keyword in its User-Agent header or by a token scoped to it. The person is whoever the calling application names in a header or a JWT, and that is optional until an administrator enforces it. A decision made about a header is only as good as the header.

Delegation is scoped, not owned

For multi-agent systems, QuilrAI checks each handoff so a child agent never receives more than its parent holds. That is a real control, and it scores a 3. It depends on the agent network being registered in advance, and it bounds what is passed down rather than carrying a named owner through the chain. When a scheduled agent acts at 3am, the useful question is not which permissions it inherited but which person is accountable for the action.

QuilrAI's gateways and sensors, read against the table's three axes

The table groups its rows by runtime, identity and coverage. QuilrAI's design, two gateways and two device sensors under one decision engine, gives a different answer to each.

  • RuntimeQuilrAI decides before the call lands. The MCP gateway rules at session, discovery, request and response, and the LLM gateway scans prompts on the way in and completions on the way out. An action that crosses neither gateway nor a managed device has no QuilrAI decision point.
  • Identitythe Guardian's verdict is only as specific as what reaches it: a User-Agent keyword or scoped token for the agent, and an email header or JWT for the person, supplied by the calling application. Neither field records who answers for the agent.
  • CoverageQuilrAI's reach is the sum of its sensors: the endpoint daemon on macOS and Windows, the browser extension, SaaS connectors and the two gateways. An agent on an unmanaged server, or a customer-facing agent whose traffic is never pointed at a QuilrAI endpoint, sits outside all of them.

The bill follows the same lines. QuilrAI licenses its browser and endpoint sensors by count, from a 100-license floor, and meters LLM and MCP gateway calls per unit, so spend tracks devices and call volume. Agen is licensed per governed agent, so spend tracks the agents themselves.

The scored comparison

Sixteen capabilities, QuilrAI and Agen.co side by side.

Runtime enforcement, identity and accountability, coverage, and what it costs to operate — each scored 0–5 on capability depth against vendor documentation, including the rows QuilrAI wins.

Capability depthNoneCompleteQuilrAIAgen.co
01 · Runtime enforcement
Verdict rendered at the moment of actionQuilrAIInline where it fronts trafficStrong4/5Agen.coPer-action verdicts, <30msComplete5/5
Decision latency, published and measuredQuilrAIDocs ~40ms; Guardian adds ~700msStrong4/5Agen.co<30ms, published, no samplingComplete5/5
Human-in-the-loop approval on a risky actionQuilrAIApproval on MCP tool callsCapable3/5Agen.coHuman-in-the-loop, built inComplete5/5
Blocks a single action without disabling the agentQuilrAIDenies the single tool callComplete5/5Agen.coAction-level enforcementComplete5/5
02 · Identity & accountability
Agent has a first-class identity objectQuilrAIMatched by User-Agent or tokenPartial2/5Agen.coFirst-class agent identityComplete5/5
Each individual action attributed to that humanQuilrAIPer user, when identity is passedCapable3/5Agen.coAttributed per actionComplete5/5
Authority chain preserved across agent-to-agent callsQuilrAIScoped per hop, registered networksCapable3/5Agen.coFull chain, agent to agentComplete5/5
Audit record per actionQuilrAIEvery call and tool loggedComplete5/5Agen.coA record per actionComplete5/5
03 · Coverage
Endpoint enforcementQuilrAIOS-level agent, macOS and WindowsStrong4/5Agen.coAgenShield on the deviceComplete5/5
Browser enforcementQuilrAIForce-installed extension, native browser agentComplete5/5Agen.coBrowserShield, early accessCapable3/5
Network-layer enforcementQuilrAITLS inspection on managed devicesCapable3/5Agen.coNot our layer — works alongsidePartial2/5
Agentless discovery — no SDK, no self-registrationQuilrAIEndpoint agent, extension, connectorsCapable3/5Agen.coAgentless, 5 surfaces, no SDKComplete5/5
MCP tool governanceQuilrAIPer tool, per call, deny-defaultComplete5/5Agen.coMCP tools governed per callComplete5/5
04 · Operate & buy
Pricing unitQuilrAIPer license; gateway usage meteredCapable3/5Agen.coPer governed agentComplete5/5
Time to first governed agentQuilrAIOne base_url change, minutesComplete5/5Agen.coDays to a first governed agentComplete5/5
Ecosystem and marketplace breadthQuilrAIAWS listing; SI and MSSP partnersStrong4/5Agen.coFocused platform, not a marketplacePartial2/5
9 rows Agen.co leads4 tied3 rows QuilrAI leads
Levels reflect capability depth and supporting evidence. Capability descriptions based on vendor public documentation.
Get the walkthrough

Get the scored comparison walkthrough.

Thirty minutes, row by row, including the ones we lose to QuilrAI. You leave with the same table, scored for your environment. Tell us anything we should know in the comments.

length30 minutes
formatrow by row
commitmentnone
What the table shows

Close on runtime. Apart on identity.

QuilrAI's enforcement is real, and the scores say so: four ties, three rows to them, and a 4 on verdicts at the moment of action, not a 2. Nothing about the call path is weak.

The line falls at identity. QuilrAI's documented agent record is a display name and a User-Agent keyword, the person is whatever the calling application passes, and no field names an owner who answers for the agent. Each card below follows that line through one group of the table, from the gateways' verdicts to the sensors QuilrAI bills for.

Reading the table

QuilrAI's call path, group by group.

One card per table group: how QuilrAI's gateways and device sensors behave once an agent is running against your systems, and where the agent's owner drops out of the record.

01

Inline on the calls it fronts

MCP tool calls and LLM requests routed through the gateways are judged before they land, and one call can be denied while the agent keeps working. Human approval is a policy effect on MCP tool calls; an action outside the gateways and the managed device has no decision point.

human approvalMCP calls
02

The agent is a header or token, not an identity

Agents are registered by name and matched by a User-Agent keyword or a scoped token. The human comes from an email header or a JWT that the calling application supplies, optional until enforced. Nothing in the documented agent record names an owner who answers for it.

agent matched byheader or token
03

Discovery runs through installed sensors

The endpoint agent, browser extension and SaaS connectors reach a long way, including local models and community MCP servers on developer laptops. Each has to be deployed or connected first, so an agent outside every sensor's reach is not inventoried.

discoverysensor-based
04

Licensed by sensor, metered at the gateway

The browser extension and the endpoint agent are separate contract dimensions, licensed by count with a 100-license minimum, and LLM and MCP gateway calls are metered per unit on top. The bill follows devices and call volume rather than the number of agents being governed.

billed persensor + call
Watch it happen

Past the header, to the person who answers for each action.

Not the user a header names — a named accountable owner carried on every agent, and every individual action attributed back to them, including the agents that run with nobody watching.

agent ownership · live product scene
What closes the gap

Keep the gateway. Add the identity behind it.

Your gateways can keep routing models and compressing tokens. Agen governs above the path: every agent discovered without a sensor to install, every action judged against your policy at the moment it happens, every verdict resolved to the human behind the agent.

Select a capability

Continuous discovery across your IdP, gateway, devices, cloud, and registries. Nothing has to self-register: agents are found, risk-scored, and resolved to a named human before they act.

  • Agentless and API-based across five surfaces
  • First-party, custom, and third-party agents alike
  • Every agent mapped to an owner, approver, and escalation
Agen Discover AI agent discovery flow: any identity provider, productivity suites and assistants, cloud, gateway, endpoints, and MCP server registries all feed one agent registry where every AI agent is inventoried, risk-scored on arrival, given a named human owner, and shadow AI is surfaced.
Discovery pulls from five surfaces into a single agent registry — no SDK self-registration required.
ClosesWhich agents are running that nobody registered?
no SDK required5 surfacesowner-mapped
Discover in depth →

Methodology

How these scores were reached.

Forty-two capabilities were assessed against the ladder in our internal rubric; thirty-seven were scored and sixteen are published here. Five were dropped and are not published on any page — three because the only finding was an absence in public documentation, which is weaker evidence than a documented limit, one because no public price list exists to score total platform cost against, and one because our own score on that row is still being verified. Each score reflects capability depth in the vendor's best available configuration — where a capability requires a separate SKU, it is scored at its real depth and the licensing cost is carried in the Operate & buy group instead of penalised twice.

Sources15 primary
Evidence ledger
  1. QuilrAI — platform overviewTier AA dedicated Guardian per agent enforcing least-privilege permissions on each tool call; violations blocked mid-chain before the request reaches data; under-50ms runtime enforcement claimed.
  2. QuilrAI — platformTier ADesign-time discovery across endpoints, browsers and MCP chains; runtime LLM and MCP gateways through one decision engine with block, allow and modify decisions; identity-provider integration via Entra ID, JWT and JWKS; embedded SaaS AI governed through connectors.
  3. QuilrAI — FAQTier AIntegration by one base_url change with no SDK; first agents surfaced within about 15 minutes; cloud, VPC or on-premise deployment; every agent action, permission decision and violation logged; PII, credentials and MNPI blocked or redacted in payloads.
  4. QuilrAI — endpoint agentTier AA system daemon on macOS and Windows that hooks AI tool processes at the operating-system level, governing file reads, shell commands and context-window secrets for coding agents.
  5. QuilrAI — third-party AITier AFour touchpoints: an endpoint agent with TLS inspection and DLP, a browser extension scanning prompts and form posts, the MCP gateway, and a compliance API syncing enterprise chat-assistant data.
  6. QuilrAI — multi-agent systemsTier AAgent networks are registered with each agent's role; each handoff is intercepted and delegated permissions are checked so a child agent never exceeds its parent's scope.
  7. QuilrAI docs — MCP gateway policiesTier AFour decision stages; rules match on caller, agent, tool annotations and data found; tool calls denied individually or paused for human approval; deny wins, with default-deny on an unregistered server.
  8. QuilrAI docs — agents configurationTier AAgents are registered by display name and identified by a keyword matched in the User-Agent header; MCP access is toggled per agent.
  9. QuilrAI docs — identity awareTier AThe user behind a gateway call is identified from an email header or a verified JWT; identity is optional on new applications until enforcement is switched on.
  10. QuilrAI docs — availability and latencyTier AThe gateway adds about 40ms for a typical 12,000-token request; the Guardian Agent adds about 700ms per request when enabled.
  11. QuilrAI — demo requestTier BLicensing is requested by license count in bands of 100–500, 501–1,000 and 1,001+, with a stated minimum of 100 licenses.
  12. QuilrAI — partnersTier ATechnology partners, security integrations, channel resellers, global systems integrators, and managed security providers offering the product as a managed service.
  13. QuilrAI — platform for engineersTier AOne MCP URL routing to 500+ MCP servers; per-agent identity verification and OAuth brokering so agents never see raw credentials; the same PII, PHI and PCI guardrails applied to every tool call; every request and guardrail decision logged.
  14. QuilrAI docs — browser deployment and validationTier AThe browser extension is force-installed alongside a native Browser Agent on Windows and macOS through Group Policy, Microsoft Intune or Jamf Pro, then validated for policy, native process, console registration and telemetry.
  15. AWS Marketplace — QuilrAI listingTier BTwelve-month contract dimensions for the browser extension, the endpoint agent, and the two combined; LLM gateway and MCP gateway usage billed per unit outside the contract; customer-hosted deployment; the listing claims sub-30ms decisions.

Vendor capabilities change. If a row is out of date or wrong, tell us and we will re-score it — corrections are published with the date they were made.

FAQ

Questions, answered.

What is QuilrAI?
QuilrAI governs AI agents and AI usage through three enforcement points: an OpenAI-compatible LLM gateway, an MCP gateway that multiplexes hundreds of MCP servers behind one URL, and an endpoint agent with a browser extension for coding agents and chat tools on employee devices. A per-agent Guardian policy is generated from a purpose statement, and a red-team agent tests it continuously.
Does QuilrAI block agent actions at runtime?
Yes, for the calls it fronts. MCP tool calls can be hidden at discovery, denied at request, or paused for human approval, and LLM requests pass through inline guardrails. A single tool call can be refused while the agent keeps running. QuilrAI's gateway documentation puts overhead at about 40ms for a typical 12,000-token request, with the Guardian checks adding roughly 700ms when enabled; its AWS Marketplace listing claims sub-30ms decisions. This page scores the documented figures.
How does QuilrAI identify the agent and the human behind it?
Agents are registered by name and matched by a keyword in the User-Agent header, or by a bearer token scoped to that agent. The human is identified from an email header or a verified JWT that the calling application supplies, and identity is optional on new applications until an administrator enforces it. The documented agent record is a display name and a keyword, and it does not name an accountable owner.
Can Agen.co run alongside QuilrAI?
Yes. QuilrAI leads on browser and device-level network inspection, and nothing in your existing path has to be removed. Agen governs the action rather than the traffic: every agent discovered agentlessly across five surfaces, every action judged against your policy in under 30ms, and every verdict resolved to a named accountable human.
How is QuilrAI licensed?
On twelve-month contracts, with the browser extension, the endpoint agent and the combined bundle as separate contract dimensions, licensed by count with a stated minimum of 100 licenses. LLM gateway and MCP gateway usage is metered per unit on top. Contract pricing is set per customer, so total platform cost is not scored on this page. Agen is priced per governed agent with no prerequisite tier.
Where does QuilrAI score better than Agen.co?
Three rows of sixteen outright, and four more are level. Browser enforcement, through a force-installed extension and a native browser agent rolled out to managed Windows and macOS devices by Group Policy, Intune or Jamf. Network-layer enforcement, through TLS inspection on managed devices, a layer we deliberately do not operate. Partner reach across a cloud marketplace, resellers, integrators and managed security providers. Level with us on blocking a single action, the per-action audit record, MCP tool governance, and time to a first governed agent.
Does this cover agents that never go through a gateway?
QuilrAI reaches those agents when its endpoint agent, browser extension or a SaaS connector is in place. Agen discovers agents agentlessly across five surfaces — identity provider, gateway, devices, cloud and registries — so an agent does not have to be routed anywhere, carry a sensor, or present a recognisable header to be found, owner-mapped and governed. Internal and customer-facing agents run on the same policy plane across endpoint, browser, gateway and cloud.
How current is this comparison?
Each score is rechecked against QuilrAI's public documentation on a fixed internal cadence, and re-scored whenever that documentation changes what QuilrAI can do. Each published row traces to at least one primary QuilrAI source listed in the methodology block, so any score can be checked against QuilrAI's own documentation.

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Tell us which QuilrAI rows you would score differently. We will walk you through the documentation behind each score, and where the evidence says otherwise, the page changes.

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