Comparison · agent governance · WitnessAI

WitnessAI, measured.

Q: What is WitnessAI?

It is an AI governance product built around three modules over the same network traffic. Observe discovers AI usage across a catalogue of more than four thousand applications with no endpoint client or browser extension. Control applies intent-based policy to that traffic, including routing, allowed-tool lists for agents, and organization-level bans. Protect provides bidirectional runtime defence: blocking prompt injection before it reaches a model, filtering harmful output, and tokenizing sensitive data on the way in.

Q: Does WitnessAI block AI actions at runtime?

Yes, within a defined scope. Enforcement is inline and bidirectional — prompt-injection attempts are blocked before reaching a model and outputs are filtered before reaching a user — and tool access is governed by allow and ban lists that an agent cannot route around by switching providers. The unit of enforcement is the AI interaction as it crosses the monitored path, so an action an agent takes against a system off that path has no decision point. No decision latency figure is published.

Q: Can Agen.co run alongside WitnessAI?

Yes. Network-layer enforcement is a layer we deliberately do not operate, and it is one of the two rows on this page where WitnessAI scores above us. 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. Nothing in your existing path has to be removed.

Q: How does WitnessAI identify the human behind an agent?

By the session. Human and agent identities are connected at runtime, and when agents call other agents those interactions are attributed back to the human who triggered the original action. That is a real authority chain and it scores a 3. It depends on there having been a triggering human session, so an agent running on a schedule has none to resolve to, and because agents are recognised from their traffic rather than enrolled as identity objects, no standing owner is carried between sessions.

Q: How is WitnessAI priced?

Per user for the platform, against a published seat minimum on a twelve-month contract, with a separate per-agent SKU covering agentic visibility and a separate flat-fee SKU for model protection. Because agents are not deployed per employee, the platform cost tracks headcount rather than the number of agents being governed, and full coverage means more than one contract. Agen is priced per governed agent with no prerequisite tier.

Q: Where does WitnessAI score better than Agen.co?

Two rows of sixteen outright, and three more are level. Network-layer enforcement, because that is the layer the product is built on and one we do not operate. Ecosystem and marketplace reach. Level with us on masking and redaction at action time, on the per-action audit record, and on data-loss protection — tokenization before a model receives the data is a complete capability and is scored as one. Browser enforcement goes to us at 3–2 only because BrowserShield is still in early access.

Q: Does this cover agents that do not go through a proxy?

That is the difference the table measures. Agen discovers agents agentlessly across five surfaces — identity provider, gateway, devices, cloud and registries — so an agent does not have to be routed anywhere, enrolled, or carry an SDK to be found, owner-mapped and governed. Internal and customer-facing agents run on the same policy plane across endpoint, browser, gateway and cloud.

Q: 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.

WitnessAI governs AI from the network path. This page scores that architecture across 16 capabilities against the same capabilities in Agen.co — with the sources, the scoring ladder, and the rows WitnessAI wins all on the page.

Talk to us Jump to the comparison

The short answer

WitnessAI governs the AI conversation in transit — the prompt, the response, and the tool calls visible in that traffic. An agent action that never crosses the monitored path is not evaluated.
It blocks inline and tokenizes sensitive data before a model receives it. Decision latency is not published. Agen renders a verdict on every action, against any policy you write, in under 30ms.
Attribution follows the session that started the work. An agent running on a schedule has no originating human session to resolve to. Agen resolves each individual action to a named accountable human.
Agents are recognised from their traffic rather than enrolled as identity objects, so there is no standing owner carried between sessions.
The platform is licensed per user with a per-agent visibility SKU sold alongside it. Agen is licensed per governed agent, in one console.
WitnessAI is stronger at the network layer, which we deliberately do not operate, and in marketplace reach. Tokenization, the audit trail and data-loss protection score level with ours.

The long read

What WitnessAI actually governs.

The architecture is the argument. WitnessAI sits in the path AI traffic already takes and makes its decisions there — which is what makes it fast to deploy, and what draws the line around the set of things it can decide about.

The three modules

The product is presented as three layers over the same traffic, and each one is worth scoring separately.

Observenetwork-level discovery against a catalogue of over four thousand AI applications, with no endpoint client and no browser extension required. It reaches native desktop applications as well as browser use, and it distinguishes agentic sessions from ordinary chat by reading tool advertisements in the traffic itself.
Controlintent-based policy over that traffic: routing by risk, cost and purpose, allowed-tool lists for agents, and organization-level bans that a team admin cannot re-enable. Audit records carry the user, the agent, the tool and the rule that fired.
Protectbidirectional runtime defence — prompt-injection and jailbreak attempts blocked before they reach a model, harmful or off-brand output filtered before it reaches a user, and sensitive data tokenized on the way in and detokenized on return.

Three deployment models carry all of it: proxy chaining onto existing secure-edge infrastructure, direct API integration, and an agentless option for the endpoints that never traverse a corporate proxy. Nothing is installed on the device.

Where WitnessAI is strongest

Two of the sixteen rows below go to WitnessAI and three are level. They follow from a real architectural difference: the traffic path is a layer they own and we deliberately do not.

Network-layer enforcementthe layer the whole product is built on. We govern the action rather than the path to the model, and run alongside whatever already sits in that path rather than replacing it.
Data protection on the way to the modeltokenization before a model receives the data, detokenization on return, and a purpose-built detection model behind it. A complete capability, scored level with ours.
Ecosystem and marketplace reachcloud-marketplace listings and secure-edge partnerships give them a wider distribution footprint than ours today.

Where the traffic layer ends

An agent does two kinds of things. It talks to a model, and it acts on a system — writes a record, moves money, sends a message, calls an internal API that has nothing to do with AI. The first kind of work is traffic, and traffic is exactly what a path-based product is good at. The second kind is an action, and an action is only visible from the path if it happens to travel it.

This is not a gap that more policy fixes. It is where the architecture draws its line, and it is the reason runtime enforcement scores a 3 rather than a 5: real inline blocking, on a bounded set of things to block.

Traced is not the same as accountable

Attribution is the strongest part of the identity story here, and it is genuinely good: an agent-to-agent chain is traced back to the human who triggered the original action, and the audit record names the user, the agent, the tool and the rule.

The dependency is the word triggered. It resolves to whoever started the session. An agent that runs on a schedule, or one started by another system, has no originating session to point at — and because agents are recognised from their traffic rather than enrolled as identity objects, there is no standing owner recorded between sessions either. A trace tells you what happened. An owner is who answers for it.

Three tests any agent-governance layer has to pass

Agent governance is a crowded word. These three questions separate a layer that governs from a layer that observes, and they are the axes the table is organised around.

Be at runtimedoes a decision get made at the moment the agent acts, or after the fact? A record of what an agent did is not the same as a verdict on whether it may.
Know the identitycan you resolve any individual action back to a named, accountable human? Not the session that started it — the person answerable for this action.
Cover everythingdoes it reach every agent, including the ones that were never routed through a proxy, act on systems rather than models, or face your customers rather than your employees?

Cost is the fourth axis, and it is the one that decides most deals. A governance layer priced per employee gets more expensive as you hire, not as you deploy agents.

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 WitnessAI wins.

Capability depthNoneComplete
01 · Runtime enforcement
Verdict rendered at the moment of action	WitnessAIInline on the AI traffic pathCapable3/5	Agen.coPer-action verdicts, <30msComplete5/5
Decision latency, published and measured	WitnessAINot publishedPartial2/5	Agen.co<30ms, published, no samplingComplete5/5
Masking or redaction at action time	WitnessAITokenized before the model sees itComplete5/5	Agen.coMasking at action timeComplete5/5
Blocks a single action without disabling the agent	WitnessAIBlocks the request or the toolCapable3/5	Agen.coAction-level enforcementComplete5/5
02 · Identity & accountability
Agent has a first-class identity object	WitnessAIIdentified from traffic, not enrolledPartial2/5	Agen.coFirst-class agent identityComplete5/5
Each individual action attributed to that human	WitnessAITraced to the triggering userCapable3/5	Agen.coAttributed per actionComplete5/5
Authority chain preserved across agent-to-agent calls	WitnessAITraced back to the first userCapable3/5	Agen.coFull chain, agent to agentComplete5/5
Audit record per action	WitnessAIImmutable trail: user, agent, tool, ruleComplete5/5	Agen.coA record per actionComplete5/5
03 · Coverage
Endpoint enforcement	WitnessAIEndpoint traffic, no device clientPartial2/5	Agen.coAgenShield on the deviceComplete5/5
Browser enforcement	WitnessAINo extension; covered on-pathPartial2/5	Agen.coBrowserShield, early accessCapable3/5
Network-layer enforcement	WitnessAIThe layer it is built onComplete5/5	Agen.coNot our layer — works alongsidePartial2/5
MCP tool governance	WitnessAIPer server and per toolCapable3/5	Agen.coMCP tools governed per callComplete5/5
Data-loss protection on agent actions	WitnessAITokenization with NER-D detectionComplete5/5	Agen.coLeak blocking at action timeComplete5/5
04 · Operate & buy
Pricing unit	WitnessAIPer user; per agent add-onCapable3/5	Agen.coPer governed agentComplete5/5
Platform cost to govern every agent	WitnessAISKUs stack; seat-based minimumsPartial2/5	Agen.coScales with agents, not headcountStrong4/5
Ecosystem and marketplace breadth	WitnessAICloud marketplace, SSE partnersCapable3/5	Agen.coFocused platform, not a marketplacePartial2/5

11 rows Agen.co leads3 tied2 rows WitnessAI leads

Levels reflect capability depth and supporting evidence. Capability descriptions based on vendor public documentation, August 2026.

What the table shows

One boundary explains most of the gap.

WitnessAI's enforcement is real, and the scores say so: a 3 on runtime enforcement, not a 1, and three rows level with ours. Nothing about the traffic layer is weak.

What it cannot reach is the action an agent takes on a system that is not on the path — and the identity it resolves to is the session that started the work rather than a standing accountable owner. Everything else follows from those two boundaries and from the unit the platform is priced in. 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.

Enforcement stops where the traffic does

Prompt-injection blocking and output filtering are genuinely inline, and organization-level tool bans cannot be routed around. But the unit of enforcement is the interaction in transit, so an agent action against a system off the path has no decision point — and no decision latency is published.

published latencynone

A traced session is not an accountable owner

Agent-to-agent work traces back to the human who triggered it, which scores a 3 and deserves it. But it resolves to whoever started the session, not to a standing owner — so a scheduled or system-started agent has nobody to resolve to at all.

standing owner per agentno

Everything has to cross the path

Endpoints and browsers are covered without installing anything, which is a real deployment advantage and an enforcement boundary at the same time. There is no device-level control point, so what never traverses the monitored path is not governed.

device-level enforcementnone

Priced per employee, with the agent SKU alongside

The platform is licensed per user against a published seat minimum, so the bill tracks headcount rather than agents. A per-agent SKU exists next to it and covers visibility, which means agent governance and agent pricing sit in two different contracts.

billed peruser

Watch it happen

Every action resolved to the human who answers for it.

Not the session that started the work — 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 proxy. Add the layer above it.

Your secure-edge path keeps inspecting traffic and your data controls keep doing classification. Agen governs on top: every agent discovered without needing to be routed anywhere first, 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 →

Every request is evaluated against the identity behind the agent and answered in under 30ms: allow, mask, step up, hand to a human, or deny. The action is where the risk lives, so the action is where the decision happens.

Per-action verdicts anchored to the human behind the agent
Step-up and human-in-the-loop only when policy is crossed
Evidence generated at action time, not reconstructed later

Agen Govern runtime AI agent governance flow: an attempted action such as an assistant reading a document, an agent exporting records, or a custom agent writing to production is resolved to the identity behind the agent and checked against policy in 22 milliseconds, returning a per-action verdict of allow, mask, step-up, human-in-the-loop, or deny. — Every agent action is judged in-line against the human behind it, and answered in under 30ms.

ClosesWho approved this action, and who is accountable for it?

<30ms verdictstep-uphuman-in-the-loop

Govern in depth →

One policy plane enforced identically on the endpoint, in the browser, at the gateway, and in the cloud. Agent risk lives in the gaps between point tools, so there are no gaps to live in.

AgenShield stops dangerous local actions before they execute
BrowserShield catches company data at the paste (early access)
One policy, one identity chain, enforced the same way everywhere

Agen Shield AI agent enforcement flow: one policy plane written once in Govern and anchored to the same identity is enforced identically on the endpoint with AgenShield, in the browser with BrowserShield, at the gateway across 150+ connectors, and in the cloud across SaaS and internal apps. — One policy, written once, enforced the same way on every surface where agents actually run.

ClosesWhat happened on the laptop and in the browser that no log saw?

endpointbrowsergateway · cloud

Shield in depth →

Methodology

How these scores were reached.

Forty-one capabilities were scored against the ladder in our internal rubric; 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, and two because the two products count the underlying thing differently enough that the row would not have been a fair like-for-like. 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.

Sources7 primary

Evidence ledger

WitnessAI — platform overviewTier AObserve, Control and Protect as the three modules; shadow AI discovery, prompt-injection blocking, output filtering, sensitive-data redaction and MCP server and tool control; agent activity attributed to human identities; NER-D named as the context-aware sensitive-data detection model.
WitnessAI — productTier ANetwork-level scanning for AI usage; intent-based policies and routing by risk, cost and purpose; agent tool-access governance where organization-level bans cannot be re-enabled by a team admin or routed around by an agent switching providers; audit records covering user, agent, tool and rule.
WitnessAI — ObserveTier AA catalogue of over 4,000 AI applications; visibility across the network without relying on browser extensions or endpoint clients, including native applications.
WitnessAI — ProtectTier ABidirectional runtime defence blocking prompt injection before it reaches models and filtering outputs before they reach users; block, filter and tokenize as the named enforcement actions; agent guardrails for autonomous systems. No latency figure, approval workflow or audit-only rollout mode is described.
WitnessAI — agentic securityTier AAgentic sessions distinguished from chat by analysing tool advertisements in traffic payloads rather than by an SDK; human and agent identities connected at runtime; agent-to-agent interactions attributed to the human who triggered the original action; MCP servers fingerprinted and classified by intent and function.
AWS Marketplace — WitnessAI listingTier BThree twelve-month contract dimensions: the enterprise platform priced per user against a 1,000-user minimum, agentic visibility priced per agent against a 2,500-agent minimum, and model protection as a flat annual fee. Delivery method is SaaS.
WitnessAI — deployment overviewTier AThree deployment models — proxy chaining onto existing secure-edge infrastructure, API integration, and an agentless option — adding governance without re-architecting the network security stack; single-tenant deployments with customer-held encryption keys and multi-region hosting; an immutable audit trail with full identity attribution; tokenization before data reaches any model and detokenization on return.

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 WitnessAI?

It is an AI governance product built around three modules over the same network traffic. Observe discovers AI usage across a catalogue of more than four thousand applications with no endpoint client or browser extension. Control applies intent-based policy to that traffic, including routing, allowed-tool lists for agents, and organization-level bans. Protect provides bidirectional runtime defence: blocking prompt injection before it reaches a model, filtering harmful output, and tokenizing sensitive data on the way in.

Does WitnessAI block AI actions at runtime?