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Comparison · agent governance · WitnessAI

WitnessAI, measured.

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.

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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 judges each action itself, including the ones that never cross a proxy, 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 carries a named owner on the agent itself, so even the scheduled run resolves to a person.
  • 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 bills per governed agent, so cost follows the agent count rather than the seat count.
  • 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 questions to put to a proxy-layer product

Observe, Control and Protect all read the same traffic, so the useful questions are about what that traffic contains and what it leaves out. The table is organised around three of them.

  • Be at runtimeWitnessAI decides inline on the prompt, the response and the tool call it can see. The question is whether the write to a ledger or the call to an internal API that follows gets a verdict too, or only a line in the audit trail.
  • Know the identitya chain traced back to the user who triggered it is real attribution. The question is who answers for the run that nobody triggered — the scheduled job, the agent started by another system.
  • Cover everythingproxy chaining and the agentless option reach whatever crosses the monitored path. The question is the agent that never does: one acting on systems rather than models, or one facing your customers rather than your staff.

Cost is where the architecture shows up on the invoice. The platform is metered per user against a seat minimum, with agent visibility on a separate per-agent SKU, so the bill grows with headcount while the thing being governed grows with deployments.

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 depthNoneCompleteWitnessAIAgen.co
01 · Runtime enforcement
Verdict rendered at the moment of actionWitnessAIInline on the AI traffic pathCapable3/5Agen.coPer-action verdicts, <30msComplete5/5
Decision latency, published and measuredWitnessAINot publishedPartial2/5Agen.co<30ms, published, no samplingComplete5/5
Masking or redaction at action timeWitnessAITokenized before the model sees itComplete5/5Agen.coMasking at action timeComplete5/5
Blocks a single action without disabling the agentWitnessAIBlocks the request or the toolCapable3/5Agen.coAction-level enforcementComplete5/5
02 · Identity & accountability
Agent has a first-class identity objectWitnessAIIdentified from traffic, not enrolledPartial2/5Agen.coFirst-class agent identityComplete5/5
Each individual action attributed to that humanWitnessAITraced to the triggering userCapable3/5Agen.coAttributed per actionComplete5/5
Authority chain preserved across agent-to-agent callsWitnessAITraced back to the first userCapable3/5Agen.coFull chain, agent to agentComplete5/5
Audit record per actionWitnessAIImmutable trail: user, agent, tool, ruleComplete5/5Agen.coA record per actionComplete5/5
03 · Coverage
Endpoint enforcementWitnessAIEndpoint traffic, no device clientPartial2/5Agen.coAgenShield on the deviceComplete5/5
Browser enforcementWitnessAINo extension; covered on-pathPartial2/5Agen.coBrowserShield, early accessCapable3/5
Network-layer enforcementWitnessAIThe layer it is built onComplete5/5Agen.coNot our layer — works alongsidePartial2/5
MCP tool governanceWitnessAIPer server and per toolCapable3/5Agen.coMCP tools governed per callComplete5/5
Data-loss protection on agent actionsWitnessAITokenization with NER-D detectionComplete5/5Agen.coLeak blocking at action timeComplete5/5
04 · Operate & buy
Pricing unit — what you are billed perWitnessAIPer user; per agent add-onCapable3/5Agen.coPer governed agentComplete5/5
Platform cost to govern every agentWitnessAISKUs stack; seat-based minimumsPartial2/5Agen.coScales with agents, not headcountStrong4/5
Ecosystem and marketplace breadthWitnessAICloud marketplace, SSE partnersCapable3/5Agen.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.
Get the walkthrough

Get the scored comparison walkthrough.

Thirty minutes, row by row, including the ones we lose to WitnessAI. 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

Two boundaries: the path, and the session.

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.

The first boundary is the path: an action an agent takes on a system the proxy never sees has no decision point. The second is the session: attribution resolves to whoever started the work, not to a standing owner of the agent. Add a per-user meter, and the cards below follow group by group.

Reading the table

What the proxy layer means for a running agent.

One card per table group: where WitnessAI's inline traffic controls hold, and where an agent's work steps off the monitored path.

01

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
02

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
03

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
04

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 →

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. WitnessAI sells agentic visibility and model protection as separate SKUs; each capability is scored at the depth its best SKU delivers, and the cost of stacking them is carried once, in the Operate & buy group.

Sources7 primary
Evidence ledger
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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?
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.
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. Your secure-edge path and WitnessAI's proxy chain keep inspecting AI traffic; Agen adds a verdict on the agent's actions, including the ones off that path, each resolved to the agent's named owner rather than to the session. Neither product has to give anything up.
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.
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.
Where does WitnessAI score better than Agen.co?
On two of the sixteen rows, and three more come out level. Network-layer enforcement goes to them, because that is the layer the product is built on and one we deliberately leave to the proxy already in place. Ecosystem and marketplace reach goes to them on the strength of cloud-marketplace listings and secure-edge partnerships. 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.
Does this cover agents that do not go through a proxy?
That is the difference the table measures. WitnessAI sees an agent when its traffic crosses the monitored path. Agen finds agents from the identity provider, devices, cloud and registries as well as the gateway, so an agent calling an internal API directly, or one serving your customers, is still found, given an owner and governed.
How current is this comparison?
Every WitnessAI cell traces to one of seven sources listed on this page — six of them WitnessAI's own product pages and blog, one its cloud-marketplace listing. The marketplace listing is the one most likely to move, so pricing cells are re-checked first on a fixed internal cadence, and any cell can be verified against WitnessAI's own wording.

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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