Comparison · agent governance · Zenity

Zenity, measured.

Zenity reviews an agent before it runs and enforces on the platforms it integrates with. This page scores that architecture across 16 capabilities against the same capabilities in Agen.co — with the sources, the scoring ladder, and the rows Zenity wins all on the page.

Talk to usJump to the comparison

The short answer

  • Zenity's strongest work happens before an agent runs: configuration, permissions, tool access and memory are reviewed against policy, and agents nobody signed off on are found. That posture review is a first-class module and it scores a 5.
  • At runtime it evaluates an action and lets it through, blocks it, or shuts the agent down. Inline prevention is generally available inside Copilot Studio and in preview for Azure AI Foundry, so enforcement depth follows the platform integration.
  • The verdict set is let through, block, or quarantine. There is no approval or step-up outcome, so a risky action is stopped rather than routed to a person. Agen renders five verdict types on every action in under 30ms.
  • Agent identity is an inventory record correlated to Okta and Entra, and the builder is surfaced per agent. Agen issues a first-class agent identity with a named accountable owner and attributes every individual action back to them.
  • Coverage is SaaS, cloud and endpoint. There is no browser control point, and nothing documented for the agents a company exposes to its own customers.
  • Pricing is a private offer with no published unit. Agen is licensed per governed agent, in one console, and deploys as SaaS, hybrid or on-prem.
The long read

What Zenity actually governs.

The architecture is the argument. Zenity builds an inventory of every agent, judges each one's configuration before it goes live, and then enforces at runtime wherever it has a hook into the platform the agent runs on — which is what makes the posture work so strong, and what draws the line around the enforcement.

The three pillars

The product is presented as three pillars over one inventory, each carrying forward what the last learned about an agent. They are worth scoring separately, because they are not equally deep.

  • Surfacea live inventory of agents across agentic SaaS, cloud and homegrown frameworks, and coding and personal agents on employee devices — including the ones security never signed off on. It records ownership, permissions, tool integrations and memory use, and it validates which of an agent's attack paths are actually exploitable rather than merely present.
  • Enforcepolicy applied at execution: a boundary engine that evaluates an agent action against the rules and lets it through, blocks it, or shuts the agent down. The same engine governs tools reached over MCP, where agents register a single gateway URL and every call is allowed, modified or blocked on live context.
  • Protectruntime detection and response — behaviour correlated against known threat frameworks, triage, and blocking of unsafe actions in real time.

It is delivered as SaaS. Identity is read from Okta and Microsoft Entra rather than issued, and used as policy context.

Where Zenity is strongest

Three of the sixteen rows below go to Zenity and two are level. They follow from a real difference in where each product places its centre of gravity: theirs is the agent's configuration and the platform it was built on, ours is the action.

  • The review before the agent runsconfiguration, permissions, tool access and memory judged against policy before anything goes live, with exploitable attack paths separated from theoretical ones. That is a full lifecycle stage ahead of where we govern, and on that row they score a 5 to our 2.
  • Depth inside the productivity suitegenerally-available native controls inside Copilot Studio are a closer integration than we have. We govern the suite from outside it, 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.
  • Ecosystem and marketplace reachcloud-marketplace listings and platform partnerships give them a wider distribution footprint than ours today.
  • Level with us on two rowsshadow-agent discovery and MCP tool governance. Finding agents nobody registered, and deciding per tool call on live context rather than a static allow-list, are complete capabilities in their product and are scored as such.

Enforcement follows the integration

A boundary engine that judges every action is the right shape for the problem. The question a buyer has to ask is where that engine gets to sit. Inline prevention is generally available inside Copilot Studio and in preview for Azure AI Foundry; MCP tool calls are governed at a gateway agents opt into; coding agents on a laptop are reached through native hooks and OpenTelemetry.

Each of those is a real control point, and each is a different integration. An agent on a platform without one is inventoried, assessed and monitored rather than stopped — which is why runtime enforcement scores a 3 rather than a 5, and why no decision latency is published.

The verdict vocabulary draws the second line. An action is let through, blocked, or the agent is quarantined. Nothing in the published set hands the decision to a person, holds it for approval, or asks for a step-up first — so a risky action that a human would have approved is stopped the same way one that should never happen is.

Correlated is not carried

Identity here is read, not issued. Agent records are correlated to the accounts already in Okta and Entra, the person who built an agent is surfaced alongside it, and the boundary engine knows who is acting and who they are acting for within a session. As policy context that is genuinely useful, and it scores a 3 rather than a 1.

What it does not do is carry an accountable owner. A builder discovered from a platform's own metadata is a fact about how the agent was made, not a standing commitment about who answers for what it does — and for an agent assembled outside the platforms in scope, there is no builder record to read. An owner is who you call at two in the morning. That is a different object from a correlation.

The agents your customers talk to

Every documented use case governs agents the enterprise runs for itself: agents inside its SaaS applications, agents its own teams built on a cloud framework, agents running on employee laptops. 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 unit. The listing is a private offer negotiated on agent count and deployment scope, which 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.

Deployment is SaaS. Where an agent handles regulated data in a jurisdiction that requires it to stay put, hybrid and on-prem are the rows to check.

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 Zenity wins.

Capability depthNoneCompleteZenityAgen.co
01 · Runtime enforcement
Verdict rendered at the moment of actionZenityInline where the platform hooksCapable3/5Agen.coPer-action verdicts, <30msComplete5/5
Decision latency, published and measuredZenityNot publishedPartial2/5Agen.co<30ms, published, no samplingComplete5/5
Human-in-the-loop approval on a risky actionZenityNot in the verdict setBasic1/5Agen.coHuman-in-the-loop, built inComplete5/5
Posture review of agent configuration before deploymentZenityReviewed before an agent goes liveComplete5/5Agen.coGoverns the action, not the configPartial2/5
02 · Identity & accountability
Agent has a first-class identity objectZenityInventory record, correlated to IdPCapable3/5Agen.coFirst-class agent identityComplete5/5
A named human accountable for each agentZenityBuilder surfaced per discovered agentCapable3/5Agen.coNamed owner, every agentComplete5/5
Each individual action attributed to that humanZenityIdentity behind each governed callCapable3/5Agen.coAttributed per actionComplete5/5
03 · Coverage
Endpoint enforcementZenityNative hooks on coding agentsCapable3/5Agen.coAgenShield on the deviceComplete5/5
Browser enforcementZenityNo browser control pointBasic1/5Agen.coBrowserShield, early accessCapable3/5
Shadow-agent discoveryZenityFinds agents nobody signed offComplete5/5Agen.coShadow AI surfacedComplete5/5
MCP tool governanceZenityPer tool call, on live contextComplete5/5Agen.coMCP tools governed per callComplete5/5
External customer-facing agentsZenityWorkforce agents, not customer-facingBasic1/5Agen.coCustomer-facing agents, same planeComplete5/5
04 · Operate & buy
Pricing unitZenityPrivate offer, unit not publishedPartial2/5Agen.coPer governed agentComplete5/5
Deployment model and data residencyZenitySaaS onlyCapable3/5Agen.coSaaS, hybrid, or on-premComplete5/5
First-party depth inside the productivity suiteZenityNative controls inside Copilot StudioCapable3/5Agen.coGoverns the suite, doesn't live in itPartial2/5
Ecosystem and marketplace breadthZenityCloud marketplace, platform partnershipsCapable3/5Agen.coFocused platform, not a marketplacePartial2/5
11 rows Agen.co leads2 tied3 rows Zenity 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.

Zenity's work before an agent runs is genuinely deeper than ours, and the scores say so: a 5 to our 2 on pre-deployment posture, and two more rows level. Nothing about the assessment layer is weak.

What changes at runtime is that enforcement depth follows the platform integration, and the identity it resolves to is correlated from your directory rather than carried as an accountable owner. 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

The verdict set stops at block

Boundaries evaluate an action and decide in real time, which is the right shape. But the published outcomes are let through, block, or quarantine the agent — so a risky action a human would have approved is handled the same way as one that should never have been attempted, and no decision latency is published.

approval verdictnone
02

A builder record is not an owner

Agents are correlated to the accounts already in your directory and the person who built each one is surfaced beside it. That is real policy context and it scores a 3. It is read from the platform's own metadata rather than carried as a standing commitment, so an agent assembled outside those platforms has no name attached at all.

standing owner per agentno
03

Three surfaces, and the browser is not one

SaaS, cloud and endpoint are covered, and shadow-agent discovery across them is as complete as ours. There is no browser control point, and nothing documented for the agents a company exposes to its own customers — the tenant-aware half of the problem sits outside the platform.

customer-facing agentsout of scope
04

A private offer, and SaaS only

Pricing is negotiated per deal with no published unit or tiers, so the cost of governing a hundred agents cannot be modelled before a sales conversation. Delivery is SaaS, which decides the answer for any workload that has to keep its data in a particular place.

published pricing unitnone
Watch it happen

Every action resolved to the human who answers for it.

Not the account that built the agent — 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 posture work. Add the layer that acts.

Your assessment of what an agent is allowed to be keeps running. Agen governs what it then does: 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 — 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 capabilities were scored against the ladder in our internal rubric; sixteen are published here. Fifteen of the sixteen come from the standard metric set every comparison on this site uses; one — posture review of agent configuration before deployment — was added because it is the capability this vendor leads on and a buyer would raise it in the room. Three rows were dropped and are not published: two because no public figure exists to compare like for like, and one 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.

Sources7 primary
Evidence ledger
  1. Zenity — platform overviewTier ASurface, Enforce and Protect as the three pillars over one inventory. Enforce evaluates every agent action in real time and lets it through, blocks it, or shuts the agent down; MCP connections are governed at the tool level; identity is correlated from Okta and Microsoft Entra. Coverage is stated as SaaS, cloud and endpoint.
  2. Zenity — AI security posture managementTier AAgent configuration and permissions evaluated against policy before deployment and continuously after. Discovery scans across SaaS platforms, custom frameworks and endpoints to build a complete inventory of agents, including ones security never signed off on. Named platforms include Copilot Studio, ChatGPT Enterprise, Salesforce Agentforce, Azure AI Foundry, AWS Bedrock and Google Vertex AI. Ownership is surfaced for every agent discovered.
  3. Zenity — MCP securityTier AAgents register one gateway URL and continue running unchanged. Per tool call the gateway can allow, modify or block on live context rather than a static allow-list, captures the user or agent behind the call, and every rule is reversible and audited. No masking, approval or step-up mechanism is described.
  4. Zenity — inline preventionTier AInline prevention is generally available in Microsoft Copilot Studio and in preview for Azure AI Foundry, delivered as native security controls that block an action before it completes. The controls are stated to introduce minimal latency; no latency figure is published.
  5. Zenity — device-based agentsTier ACoding and personal agents on employee devices are covered through native hooks and OpenTelemetry together with the MCP gateway, with enforcement able to block or modify a dangerous tool call before it executes. No browser extension or in-browser control point is described.
  6. Zenity — runtime boundariesTier AA runtime boundary is a policy engine that evaluates every agent action against an organization's rules and decides, on the spot, whether to let it through, block it, or shut the agent down; it can detect quietly, block outright, or quarantine the agent entirely, evaluating identity attributes, session history and custom risk labels, and understanding who is acting and who they are acting for. A detection-only logging mode exists for validating rules. Step-up authentication, human approval and masking are not described.
  7. AWS Marketplace — Zenity listingTier BDelivery method is SaaS. Pricing is a contract private offer with no published per-unit rate or tiers — pricing is based on the duration and terms of the contract with the vendor. Capabilities are listed as AI observability, security posture management, and detection and response over step-level agent activity.

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 Zenity?
It is an AI agent security and governance platform built as three pillars over one inventory. Surface discovers and inventories agents across agentic SaaS, cloud and homegrown frameworks, and coding and personal agents on employee devices, and assesses their configuration, permissions, tool access and memory against policy. Enforce applies runtime boundaries that evaluate an agent action and let it through, block it, or shut the agent down, including tools reached over MCP. Protect detects runtime threats, correlates behaviour against known threat frameworks and blocks unsafe actions.
Does Zenity block agent actions at runtime?
Yes, within a defined scope. A runtime boundary evaluates every agent action against policy and decides on the spot whether to let it through, block it, or quarantine the agent, and MCP tool calls can be allowed, modified or blocked on live context at a gateway. Inline prevention is generally available inside Microsoft Copilot Studio and in preview for Azure AI Foundry, so enforcement depth follows the platform integration — an agent on a platform without one is inventoried, assessed and monitored rather than stopped. No decision latency figure is published.
Can Agen.co run alongside Zenity?
Yes. Pre-deployment posture review is a row where Zenity scores above us, and it is a different question from the one we answer. Agen governs the action: every agent discovered agentlessly across five surfaces, 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 Zenity identify the human behind an agent?
By correlation. Agent records are matched to the identities already in Okta and Microsoft Entra, the account that built each agent is surfaced beside it in the inventory, and the boundary engine uses who is acting and who they are acting for as policy context within a session. That is a real capability and it scores a 3. It is read from the platform's own metadata rather than carried as a standing accountable owner, so an agent assembled outside the platforms in scope has no builder record to resolve to.
How is Zenity priced?
As a private offer. The marketplace listing publishes no per-unit rate and no tiers — pricing is based on the duration and terms of the contract, negotiated on factors including agent count and deployment scope. 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 Zenity score better than Agen.co?
Three rows of sixteen outright, and two more are level. Posture review of an agent's configuration and permissions before it goes live, which is a full lifecycle stage ahead of where we govern and where they score a 5 to our 2. First-party depth inside the productivity suite, where generally-available native controls in Copilot Studio are a closer integration than governing the suite from outside it. Ecosystem and marketplace reach. Level with us on shadow-agent discovery and on MCP tool governance, both of which are complete capabilities in their product and are scored as such.
Does Zenity 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 inside its SaaS applications, agents its teams built on a cloud framework, agents on employee devices. 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.

Talk to usSee the platform