Comparison · agent governance · Airia

Airia, measured.

Airia is where an enterprise builds its agents, and the same platform governs them as they run. This page scores that architecture across 16 capabilities against the same capabilities in Agen.co — with the sources, the scoring ladder, and the rows Airia wins all on the page.

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The short answer

  • Airia genuinely enforces at runtime. Constraints run as code at the execution layer, tool inputs are validated before an action proceeds, and unsafe data transfers are blocked as they occur. This is the closest table we publish on runtime enforcement, and the score says so.
  • Enforcement reaches what runs through the platform. Airia is the builder, the gateway and the runtime, so an agent that never calls it is inventoried and monitored rather than stopped — which is why the row scores a 4 rather than a 5, and why no decision latency is published.
  • Discovery is excellent and level with ours: seven vectors, including identity-provider logs, network traffic, code repositories and endpoint scanning, surfacing shadow AI and agents built well outside Airia.
  • Nothing in the published model names a human accountable for an agent. Agents carry a risk classification; approvals record the reviewer. Agen issues a named owner per agent and attributes every individual action back to them.
  • The catalog is a genuine strength and ties with ours at over a thousand tools and data sources, reached through an MCP gateway that governs every call.
  • Every documented use case is an agent an enterprise runs for its own staff. Agen governs those and the agents a company puts in front of its own customers, on one policy plane.
The long read

What Airia actually governs.

Most vendors in this category arrive at governance from security and reach toward the agents. Airia arrives from the other direction: it is the place the agents get built, so policy is a property of the platform they were built on. That is what makes the enforcement real, and it is what draws the line around it.

One platform, four jobs

The product is organised as four stages over a single inventory. They are worth scoring separately, because a buyer evaluating the governance is often shown the orchestration.

  • Discoveran inventory of AI across the organisation, assembled from seven independent vectors — browser activity, connected SaaS applications, API calls to model providers, code repositories, identity-provider logs, network traffic through a SASE, and scans of installed applications on endpoints. It surfaces models, tools, MCP servers and agents, including agents built outside Airia entirely.
  • Secureguardrails that inspect prompts, responses and workflow steps; constraints that control which tools an agent may invoke and validate execution parameters before an action proceeds; red teaming; and posture management that blocks unsafe data transfers and unauthorised tool access in real time.
  • Governpolicies enforced at runtime rather than reviewed after the fact, a tamper-evident audit trail of every agent decision, risk classifications applied to agents, models and data sources, and approval steps routed to roles by risk class.
  • Optimizethe build side — a visual agent builder extensible with Python, model routing, cost controls, a prototyping studio and the data integrations that connect an agent to the systems it acts on.

Where Airia is strongest

Two of the sixteen rows below go to Airia and three are level. They follow from a real difference in where each product starts: theirs is the platform the agent is built and run on, ours is the action the agent takes.

  • Reach into the tools people already work inagents ship as Microsoft Teams, Slack, SharePoint, WhatsApp and email surfaces, which is a closer integration with the productivity suite than we have. We govern those suites from outside them, which is what lets one policy also reach agents built anywhere else — on any framework, on any cloud, and in front of your own customers.
  • Catalog and marketplace breadthover a thousand pre-configured integrations, presented as the largest enterprise-ready MCP catalog, plus a cloud-marketplace listing. That is a wider distribution and integration footprint than ours today.
  • Level with us on three rowsshadow-agent discovery, connector and tool breadth, and browser coverage. Seven discovery vectors is as complete as anything in this category, their published catalog figure stands beside ours, and browser AI activity and controls are scored level with BrowserShield while ours is in early access.

Enforcement reaches what it runs

Constraints that run as code at the execution layer are the right shape for the problem, and they earn a 4 at the moment of action rather than the 2 a discovery-first product earns. Tool invocation is controlled, execution parameters are validated before an action proceeds, and policy violations are blocked as they occur. None of that is posture work relabelled.

The question a buyer has to ask is which actions pass through that execution layer. Airia is the builder, the gateway and the runtime, so for an agent built in Airia the answer is all of them. For an agent built somewhere else, calling a system directly, the platform has a rich inventory record and seven ways to have found it — and no interception point. The published material asserts that centralized policies follow your agents into every model, tool and runtime; it does not describe the mechanism by which an action that never reaches Airia is stopped.

The verdict vocabulary is strong on the two outcomes that matter most and quiet on a third. Actions are blocked, regulated data is masked, and high-risk decisions escalate to a named reviewer with the context attached. What is not described is a step-up: asking the person the agent is acting for to re-authenticate before the action proceeds, rather than routing it to whoever holds the reviewing role.

A risk class is not an owner

Airia's accountability model is built on classification. Agents, models and data sources carry a risk classification applied proactively, approvals route by that classification to the role that should see them, and every approval is logged with who reviewed what and when. For an audit, that is a genuinely strong record, and the per-decision audit trail scores level with ours.

What the model does not carry is a person answerable for the agent itself. A risk class describes what an agent is; a reviewer is whoever was on the rota when something escalated. Neither is the name you need when an agent has been running unattended for six weeks and has just done something nobody expected. The identity provider is read here as a source of signal for finding AI activity, not as the fabric an agent's own identity lives in — which is why the attribution row scores a 2 while the audit row does not.

The agents your customers talk to

Every documented use case governs agents the enterprise runs for itself: agents its teams built in the studio, agents reaching internal systems through the gateway, agents delivered to staff as a chat surface in Teams or Slack. That is one half of the problem, and it is the half most vendors in this category have chosen.

The other half is the agent a company puts in front of its own customers — the one acting on a tenant it does not employ, against a customer record it does not own. It needs the same identity fabric and the same per-action verdicts as the internal one, and it is the row where the two products differ most.

What the contract does not say

There is no published pricing. The pricing page redirects to a demo request, and the cloud-marketplace listing states that pricing is based on your specific requirements and eligibility, by private offer. That is a normal enterprise motion and is scored as what it is rather than as a criticism — but an unpublished unit is a thing a buyer cannot model before the first call, and it cannot be compared against a competitor's without one.

Two other things a security review usually asks for are absent from the public material: which identity providers are supported for single sign-on and provisioning, and which deployment models and data-residency options exist. Both were scored in the worksheet and neither is published here, because absence of evidence is not evidence of absence — they are questions to put on the call, not rows to score against a vendor.

The scored comparison

Sixteen capabilities, scored 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 Airia wins.

Capability depthNoneCompleteAiriaAgen.co
01 · Runtime enforcement
Verdict rendered at the moment of actionAiriaBlocks where Airia executesStrong4/5Agen.coPer-action verdicts, <30msComplete5/5
Decision latency, published and measuredAiriaNot publishedBasic1/5Agen.co<30ms, published, no samplingComplete5/5
Step-up authentication on a risky actionAiriaNot documentedBasic1/5Agen.coStep-up, built inComplete5/5
02 · Identity & accountability
Agent has a first-class identity objectAiriaAgent object inside AiriaCapable3/5Agen.coFirst-class agent identityComplete5/5
A named human accountable for each agentAiriaNot documentedBasic1/5Agen.coNamed owner, every agentComplete5/5
Each individual action attributed to that humanAiriaDecisions logged, approvers namedPartial2/5Agen.coAttributed per actionComplete5/5
Access evaluated at action time, not only at grant timeAiriaTool calls checked at executionCapable3/5Agen.coJudged in context, per actionComplete5/5
03 · Coverage
Endpoint enforcementAiriaEndpoint scanning, not enforcementPartial2/5Agen.coAgenShield on the deviceComplete5/5
Browser enforcementAiriaBrowser AI activity and controlsCapable3/5Agen.coBrowserShield, early accessCapable3/5
Shadow-agent discoveryAiriaSeven discovery vectorsComplete5/5Agen.coShadow AI surfacedComplete5/5
Agents built outside the vendor's own stackAiriaDiscovered; enforced when routed throughPartial2/5Agen.coAny agent, any stackComplete5/5
External customer-facing agentsAiriaInternal enterprise scopeBasic1/5Agen.coCustomer-facing agents, same planeComplete5/5
Connector and tool breadthAiria1,000+ tools and data sourcesComplete5/5Agen.co150+ connectors · 1,000+ toolsComplete5/5
04 · Operate & buy
Pricing unitAiriaCustom quote, unit not publishedPartial2/5Agen.coPer governed agentComplete5/5
First-party depth inside the productivity suiteAiriaTeams, Slack, SharePoint botsCapable3/5Agen.coGoverns the suite, doesn't live in itPartial2/5
Ecosystem and marketplace breadthAiria1,000+ catalog, one cloud marketplaceStrong4/5Agen.coFocused platform, not a marketplacePartial2/5
11 rows Agen.co leads3 tied2 rows Airia leads
Levels reflect capability depth and supporting evidence. Capability descriptions based on vendor public documentation, August 2026.
What the table shows

The boundary is the platform, not the intent.

Airia enforces at runtime, and the scores say so plainly: a 4 at the moment of action, level with us on discovery, on catalog breadth and on the browser, and ahead of us on reach into the productivity suite and on marketplace footprint. Nothing about the enforcement layer is decorative.

What bounds it is that the enforcement point and the build platform are the same thing, and that no object in the model resolves an action to a person who answers for it. Everything else follows from those two boundaries and from the surfaces in scope. Those four consequences are below.

Reading the table

Four consequences, one per group.

Each card is the practical version of a group in the table above — what the scores mean once an agent is actually running against your systems.

01

Real enforcement, on its own runtime

Constraints run as code at the execution layer and tool inputs are validated before an action proceeds — this is enforcement, not posture. It reaches the actions that pass through the platform that built the agent. No decision latency is published, and a risky action escalates to a reviewer rather than back to the person the agent is acting for.

published latencynone
02

Classified, reviewed, but not owned

Agents carry a risk classification, approvals route by that class, and every review is logged with who signed off and when. The per-decision audit trail is as complete as ours. What no record carries is a standing accountable owner, so an individual action resolves to the reviewer on the rota rather than to the person answerable for the agent.

named owner per agentnone
03

Found everywhere, governed where it runs

Seven discovery vectors surface agents built well outside the platform, and that breadth is scored level with ours. Enforcement is a narrower set than discovery: an outside agent is inventoried and monitored rather than stopped, endpoints are scanned rather than enforced on, and customer-facing agents sit outside the documented scope.

outside agentsfound, not stopped
04

Priced on request

The pricing page redirects to a demo request and the marketplace listing carries a private offer with no unit and no tiers, so the cost of governing a hundred agents cannot be modelled before a sales conversation. Deployment models and data residency are not documented publicly either — both are questions for the call.

published pricing unitnone
Watch it happen

Every action resolved to the human who answers for it.

Not the risk class it was filed under, and not the reviewer who happened to be on the rota — 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 building there. Govern from a layer that isn't the builder.

The agents your teams build in Airia keep running exactly as they do. Agen governs the action wherever it happens: every agent discovered without being routed anywhere first, every action judged against your policy at the moment it happens, every verdict resolved to the human behind the agent — internal agents and the ones facing your customers, on the same plane.

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 scored against the ladder in our internal rubric; sixteen are published here, and all sixteen come from the standard metric set every comparison on this site uses — no row was added to suit this vendor. Four rows were dropped and are not published: identity-provider compatibility, time to first governed agent, and deployment model and data residency, because no public source states them either way; and audit-first rollout mode, because our own score on it is still being confirmed with product. 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.

Sources12 primary
Evidence ledger
  1. Airia — platform overviewTier AThe platform is organised as Discover, Secure, Govern and Optimize. Named modules include AI Discovery, Security Posture, AI Inventory, Risk Classification, Red Teaming, MCP Gateway, Agent Constraints, AI Guardrails, Governance Dashboard, Compliance Reporting, System Controls, Agent Builder, Model Routing, Cost Optimization, Prototyping Studio, Model Lifecycle and Data Integrations.
  2. Airia — AI guardrailsTier AGuardrails evaluate incoming prompts before they reach the model, inspect model outputs for sensitive data before they reach users, and secure workflow interactions. Enforcement includes blocking prompt injection and jailbreak attempts, automatically masking regulated data, validating claims against approved sources, and escalating high-risk actions to a reviewer with full context. No latency figure and no step-up authentication flow is described.
  3. Airia — governanceTier APolicies are enforced at runtime, not reviewed after the fact. Every agent decision is logged in a tamper-evident audit trail available to compliance teams. Human-in-the-loop approvals can be triggered based on risk classification, data sensitivity or action type, and risk classifications are applied proactively to agents, models and data sources.
  4. Airia — AI discoveryTier ADiscovery runs across seven vectors: browser AI activity, connected SaaS applications, API calls to model providers, code repository scanning for LLM integrations and MCP servers, identity-provider logs, SASE network traffic, and scans of installed applications and local environments on endpoints. It surfaces unsanctioned AI usage and covers Airia agents, external agents, models, data sources, tools, MCP servers, apps and endpoints.
  5. Airia — MCP capabilitiesTier AThe MCP Gateway provides governed, enterprise-grade execution across over 1,000 pre-configured integrations, described as the largest enterprise-ready MCP catalog, and ensures every tool call respects policy, permissions and audit requirements.
  6. Airia — agent constraintsTier AConstraints control which tools agents can invoke and restrict the conditions under which they can act, ensure sensitive data is only accessed by approved models and workflows, and validate tool inputs and execution parameters before actions are allowed to proceed. Constraints are stated to run as code at the execution layer, with centralized policies following agents into every model, tool and runtime.
  7. Airia — system controlsTier AConfigurable approval steps are embedded directly into agent workflows, with approval tasks routed to specific roles based on risk classification and business context, and routing updated automatically as context shifts. Every approval is logged with who reviewed what, when they approved it and what changed. No role-based access control model, identity-provider integration or agent-ownership mechanism is specified.
  8. Airia — security posture managementTier AThe platform automatically blocks unsafe data transfers, unauthorized tool access and policy violations as they occur, with policies enforced in real time through a centralized control layer. Detection spans identity-provider and single-sign-on activity, web AI activity and controls, endpoint apps and local runtimes, SASE traffic and data flow, and API and code repository monitoring.
  9. Airia — securityTier ALists AICPA SOC, COPPA, FERPA, GDPR, EU-US Data Privacy Framework, HIPAA, ISO and PCI compliance, alongside Security Posture Management, Agent Constraints, Agent Red Teaming and Responsible AI Guardrails. No identity-provider integrations, agent authentication model, credential handling, deployment models or data-residency options are documented.
  10. Airia — pricingTier BThe vendor's pricing URL redirects to a demo request page. No pricing tiers, billing unit or list price is published anywhere on the vendor's own site.
  11. AWS Marketplace — Airia listingTier BPricing is based on your specific requirements and eligibility, available by requesting a private offer, with no prices, billing units or contract durations listed. Delivery method is professional services. Listed capabilities include enterprise security and governance for agentic ecosystems, workflow orchestration across agents, models and data sources, and multi-agent orchestration.
  12. Airia — agent builderTier AA visual builder with model selection, secure Python extension and workflow orchestration including routers, conditional branches and loops. Cites 1,000+ tools and data sources, and ships agents as Slack, Microsoft Teams, WhatsApp, SharePoint chat widget, email inbox and webhook surfaces. No time-to-value figure is published.

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 Airia?
It is an enterprise platform for building, running and governing AI agents, organised as four stages over one inventory. Discover builds an inventory of AI across the organisation from seven vectors including browser activity, SaaS applications, API calls, code repositories, identity-provider logs, network traffic and endpoint scans. Secure applies guardrails to prompts, responses and workflow steps, constrains which tools an agent may invoke, and validates execution parameters. Govern enforces policy at runtime with a tamper-evident audit trail and risk-based approvals. Optimize is the build side — a visual agent builder, model routing, cost controls and data integrations.
Does Airia block agent actions at runtime?
Yes. This is a genuine runtime enforcement product, not posture work relabelled. Constraints are documented as running as code at the execution layer: they control which tools an agent can invoke, restrict which models and workflows may process sensitive data, and validate tool inputs and execution parameters before actions are allowed to proceed. Posture management blocks unsafe data transfers, unauthorised tool access and policy violations as they occur. The bound is which actions pass through that execution layer — Airia is the builder, the gateway and the runtime, so an agent built elsewhere and calling a system directly is inventoried and monitored rather than stopped. No decision latency figure is published.
Can Agen.co run alongside Airia?
Yes, and this is the common shape. Airia stays the place your teams build and orchestrate agents, with its catalog and its studio intact. Agen governs the action from outside the builder: every agent discovered agentlessly across five surfaces whether or not it was built in Airia, every action judged against your policy in under 30ms with five verdict types including step-up and human approval, and every verdict resolved to a named accountable human. Nothing you already run has to be removed.
How does Airia identify the human behind an agent?
Through classification and review rather than ownership. Risk classifications are applied proactively to agents, models and data sources; approvals route to roles based on risk classification and business context; and every approval is logged with who reviewed what, when they approved it and what changed. Identity-provider and single-sign-on activity is read as a signal for discovering AI usage. What the published model does not describe is a named human accountable for each agent, or an individual action attributed back to that person — which is why the audit row scores level with ours and the attribution row does not.
How is Airia priced?
It is not published. The pricing page redirects to a demo request, and the cloud-marketplace listing states that pricing is based on your specific requirements and eligibility, available by private offer with no per-unit rate, no tiers and no contract terms listed. That is a normal enterprise motion, but it means the cost of governing a given number of agents cannot be modelled before a sales conversation. Agen is priced per governed agent with no prerequisite tier.
Where does Airia score better than Agen.co?
Two rows of sixteen outright, and three more are level. Reach into the productivity suite, where agents ship as Microsoft Teams, Slack, SharePoint, WhatsApp and email surfaces — a closer integration than governing those suites from outside them. Ecosystem and marketplace breadth, on the strength of over a thousand pre-configured integrations described as the largest enterprise-ready MCP catalog, plus a cloud-marketplace listing. Level with us on shadow-agent discovery across seven vectors, on connector and tool breadth, and on browser coverage while BrowserShield is in early access.
Does Airia cover agents that face a company's own customers?
That is the row where the two products differ most. Every documented use case governs agents the enterprise runs for itself — agents its teams built in the studio, agents reaching internal systems through the gateway, agents delivered to staff in Teams or Slack. Agen governs both halves on one policy plane: the internal workforce agent and the customer-facing agent acting on a tenant the company does not employ, with the same identity fabric, the same per-action verdicts and the same accountable owner behind each.
How current is this comparison?
It is scored against vendor public documentation and re-scored on a schedule we hold ourselves to. Every published row is backed by at least one primary vendor source, all of which are listed on this page — so any row can be checked against the vendor's own docs rather than taken on trust.

Bring your own comparison.

Send us the rows you would score differently. We will show you the evidence behind ours, and where we are wrong we will change the page.

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