Comparison · agent governance · Aembit

Aembit, measured.

Aembit gives an agent a cryptographically attested identity, mints a short-lived credential for the resource it is reaching for, and injects that credential into the request — so the agent never holds a standing secret, and the human driving it rides along in the policy decision. This page scores that architecture across 16 capabilities against the same capabilities in Agen.co, with the sources, the scoring ladder, the four rows that come out level and the two Aembit wins all on the page.

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The short answer

  • This is the most competitive table we publish. Ten rows of sixteen to us, four level, and two to Aembit outright. They are not a login-time authentication layer and the table does not score them as one.
  • Blended Identity is the real thing. The agent's identity and the identity of the human operating it are evaluated together, in a single policy decision, at request time — with per-user credential isolation, so two people driving the same agent get credentials scoped to themselves. Attribution scores a 4 and delegated access ties at the top.
  • The commercial model is published, and that is a row they win. Twenty dollars per agent per month, a free tier with no time limit, and no prerequisite platform licence. The billing unit is the same as ours, so the pricing-unit row ties and total platform cost goes to them against the 4 we hold everywhere.
  • The verdict governs reach, not the action. Aembit decides whether a workload may hold a credential for a resource under the conditions in force right now, then injects it and forwards the request. What the agent then does inside that system — which record, how much of it, for whom — happens between the agent and the system.
  • Coverage is the set of agents someone onboarded. Every governed agent is a Client Workload that a person declared, reached by an Edge component that a person deployed on a host, a cluster, a pipeline or a gateway VM. That is a deliberate design with a real upside — data never leaves your network, another row they win — and it means discovery starts from a list somebody already had.
  • They do more at the moment of action than the category usually does. Content inspection on MCP tool calls returns allow, block and transform verdicts, so redaction scores a 3 rather than a 1, and blocking one message explicitly does not stop the ones that follow it.
The long read

The credential is not the action.

Aembit came to agent security from workload identity rather than from posture or from a proxy, and it shows in the parts that are hard. Attestation is cryptographic, credentials are minted per request and never held, and the human operating the agent is carried into the decision. It is worth being precise about what that decision covers, because it covers a great deal — and about where it hands off.

What the product actually is

Worth stating plainly, because the category name attracts products that do much less than this one. Aembit is a control plane and a data plane, and the split between them explains most of the table.

  • Aembit Cloudthe control plane. It holds the access policies, verifies workload identity through trust providers, evaluates conditions, decides, and coordinates credential issuance. It is where an administrator writes the rule and where the audit record lands.
  • Aembit Edgethe data plane, deployed inside the customer's own environments — a Kubernetes sidecar, an ECS task, a Linux or Windows VM, a Lambda layer, a CI/CD runner. It intercepts the outbound request a workload makes, gathers identity evidence about the process making it, asks the control plane for a credential, injects that credential into the request and forwards it. Workload traffic never passes through Aembit's infrastructure, which is a genuine advantage and a row they win.
  • The two MCP componentsan authorization server implementing the OAuth 2.1 flow from the MCP specification, which handles the human's login through an identity provider the company already runs; and an identity gateway, deployed as a Linux virtual machine, which validates the resulting token on every MCP request, enforces policy and exchanges credentials so the agent never holds one for the server behind it. Containerised and fully managed deployments are on their roadmap.
  • Blended Identitythe access model that ties it together. Rather than asking who the user is or what the workload is, a policy asks whether this particular user, using this particular agent, may reach this particular resource right now. Two people driving the same agent get credentials scoped to themselves, and either one can be cut off without touching the other.

Where Aembit is strongest

Four rows come out level and two go to Aembit. They are not consolation rows, and three of them are places most of this category has nothing at all.

  • Delegated access, and the human on iton-behalf-of access ties at the top of the scale. Credentials are exchanged per user at request time, scoped to that user's own permissions, and the agent holds neither. Every access event records who the authenticated person was, which agent made the request, what was reached and what was decided. Attribution scores a 4 — the caveat is that the human it blends in is the one operating the agent, so an agent running with nobody at the keyboard has no human to blend.
  • A published price, per agenttwenty dollars per agent per month, a free tier with no time limit, no prerequisite platform licence. The pricing-unit row ties at the top because that is the same billing unit we use — the bill tracks the population being governed rather than headcount — and the total-cost row goes to them against the 4 we score on every comparison we publish, because a free starting tier and a posted per-agent price is a shorter procurement conversation than ours.
  • Your data stays where it isthe decision is made in their cloud, but the traffic is proxied inside the customer's own network and never traverses Aembit. Level with us at the top of the scale, and for a regulated buyer with residency requirements it is often the first question rather than the last.
  • MCP governed per tool calllevel with us, and their strongest coverage row. The identity gateway enforces on every MCP request, and content inspection can additionally evaluate tool listings, tool call inputs and tool call outputs — returning allow, block, or transform, where transform forwards the content with sensitive data redacted. That inspection is delivered by a partner service configured on the policy, which is the caveat that keeps the redaction row at a 3, but it is a real verdict at the moment of the call.
  • Ecosystem reacha published stack map naming thirty-four integration vendors across secrets management, privileged access, governance and workload identity federation, plus a Terraform provider and Helm charts. Ours is a focused platform and we score it a 2 everywhere, which is a deliberate choice rather than a gap we are working on — but on this row it is theirs.

Reach is not the same thing as the action

Here is the boundary the table is actually measuring, and it is architectural rather than a matter of maturity. Aembit's decision point is the moment a request needs a credential. The question it answers is whether this workload, attested and carrying this person, may hold a credential for that resource under the conditions in force right now — the time of day, the country the request came from, whether the endpoint posture reported by a security platform is healthy. If every condition passes, a short-lived credential is minted, injected into the request and forwarded.

That is a real decision and the table scores it as one: policy scope is a 4, per-request evaluation is a 4, and the runtime row is a 3 rather than the 1 the archetype would suggest. But notice what the answer is about. It is about reach — which system, on whose behalf, from where. Once the credential is in the request and the request is on its way, the agent is inside the target system holding valid permissions, and which record it touches, how many of them it exports, whose data is in the response and whether a person ought to see it first are all decided by that system, not by Aembit.

The one place they cross that line is worth naming, because it shows what the model can do when something inspects the payload: with content inspection configured, an MCP tool call's inputs and outputs are examined and can come back redacted. It is scoped to MCP traffic and it is delivered by a partner service you configure and license separately, which is why the row is a 3 and not a 5 — but it is the exception that proves where the boundary otherwise sits.

Onboarded is not the same thing as discovered

The second half follows from the same architecture. Everything Aembit governs is a Client Workload that somebody defined, reached by an Edge component that somebody installed on a host, a cluster, a pipeline or a gateway VM. That is a deliberate and defensible design — it is why the traffic stays inside the customer's network, and it is why attestation can be cryptographic rather than a shared token.

It also sets the boundary of the estate. The agents under policy are the ones on the list, and the list is one somebody already had. Nothing in the published product material describes finding an agent nobody registered — a script somebody wrote on a Friday, an assistant a team connected to a SaaS tool with a personal token, an integration standing up in a business unit that never spoke to platform engineering. The platform is not built to look for those, and an agent that was never onboarded has no policy to evaluate and no path through Edge.

This is the row where the two approaches are furthest apart, and it is the one worth testing on your own environment rather than taking from either vendor. Connect a discovery pass and compare the count it returns against the list of agents your platform team believes exists. The gap between those two numbers is what an onboarding-first model cannot reach, and the size of it is different at every company.

What the table does not claim

Six standard rows were dropped rather than guessed. Decision latency, because no vendor page publishes a figure and scoring silence is not scoring a product. Step-up authentication and human approval, neither documented in either direction — an access condition passes or denies, and nothing in the material describes a challenge raised to a person mid-action, so the row was cut rather than scored on its absence. Agent-to-agent authority chaining, undocumented. Connector breadth, dropped on fairness: their integrations are trust, credential and posture providers, ours are governed connectors and tools, and counting them against each other would measure nothing. New-integration turnaround, unpublished. Our own audit-first rollout row is held pending internal confirmation and appears on no comparison we publish.

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 four rows that come out level and the two Aembit wins.

Capability depthNoneCompleteAembitAgen.co
01 · Runtime enforcement
Verdict rendered at the moment of actionAembitInline on every proxied requestCapable3/5Agen.coPer-action verdicts, <30msComplete5/5
Masking or redaction at action timeAembitRedaction via partner inspection serviceCapable3/5Agen.coMasking at action timeComplete5/5
Blocks a single action without disabling the agentAembitBlocks one request, agent continuesStrong4/5Agen.coAction-level enforcementComplete5/5
Policy scope — what you can write a rule aboutAembitIdentity, time, geography, posture, contentStrong4/5Agen.coAny policy you writeComplete5/5
02 · Identity & accountability
A named human accountable for each agentAembitBlended with the operating humanCapable3/5Agen.coNamed owner, every agentComplete5/5
Each individual action attributed to that humanAembitUser and agent on every requestStrong4/5Agen.coAttributed per actionComplete5/5
Delegated (on-behalf-of) accessAembitPer-user credentials, exchanged at requestComplete5/5Agen.coOn-behalf-of, governedComplete5/5
Access evaluated at action time, not only at grant timeAembitConditions evaluated on every requestStrong4/5Agen.coJudged in context, per actionComplete5/5
03 · Coverage
Endpoint enforcementAembitWorkload hosts, not user devicesPartial2/5Agen.coAgenShield on the deviceComplete5/5
Browser enforcementAembitNo browser enforcement pointBasic1/5Agen.coBrowserShield, early accessCapable3/5
Agentless discovery — no SDK, no self-registrationAembitWorkloads are configured, not discoveredBasic1/5Agen.coAgentless, 5 surfaces, no SDKComplete5/5
MCP tool governanceAembitPer tool call, with inspectionComplete5/5Agen.coMCP tools governed per callComplete5/5
04 · Operate & buy
Pricing unit — what you are billed perAembit$20 per agent, per monthComplete5/5Agen.coPer governed agentComplete5/5
Platform cost — total spend to govern N agentsAembitFree tier, published per-agent priceComplete5/5Agen.coScales with agents, not headcountStrong4/5
Deployment model and data residencyAembitData stays in your networkComplete5/5Agen.coSaaS, hybrid, or on-premComplete5/5
Ecosystem and marketplace breadthAembit34 integrations, Terraform and HelmCapable3/5Agen.coFocused platform, not a marketplacePartial2/5
10 rows Agen.co leads4 tied2 rows Aembit leads
Levels reflect capability depth and supporting evidence. Capability descriptions based on vendor public documentation, September 2026.
What the table says

A decision on every request, about which door the agent may open.

The four level rows and the two rows Aembit wins all follow from one decision, and it is a good one: attest the workload, mint the credential per request, keep the traffic inside the customer's network, and carry the human operating the agent into the policy. That is what makes per-user credential isolation, a published per-agent price and a genuine residency story possible at once, and no product that terminates traffic in its own cloud gets those for free.

The rows that separate us follow from the same decision. A platform that decides which credential a request may carry can control what an agent reaches; it cannot judge the write that credential is then used to perform, mask what comes back from a system it does not sit inside, step a person up, or hold a single action for the human it concerns. And it governs the agents that were put on the list. Agen does the other part, and it does not ask you to take any of this out to get it.

Four findings

The same argument, one card per group.

Each card takes one group of the scored table and says what the rows in it add up to.

01

The verdict decides reach

Policy is evaluated on every request that passes through an Edge component, with conditions on time, geography and endpoint posture — a real decision, and scored as one. What it decides is whether a credential is issued for a resource. Once that credential is injected and the request forwarded, the specific write happens inside the target system: the response cannot be masked, a person cannot be stepped up, and no single action can be held for approval.

decision pointcredential, not action
verdict typesallow / deny
02

The human rides on the request

Blended Identity evaluates the agent and the person operating it in one decision, at request time, with credentials isolated per user and every access event recording both. This is the strongest accountability story we have scored in this category, and two of these rows are level. The caveat is who the human is: it is whoever is driving the agent, so an agent running autonomously with nobody at the keyboard has nobody to blend in.

attributionuser and agent, per request
autonomous agentsno human to blend
03

Governs the list you already had

Every governed agent is a Client Workload someone declared, reached by an Edge component someone deployed — on a cluster, a VM, a pipeline or a gateway. MCP tool calls are governed per call and tie with us at the top of the scale. What the model does not do is find the agent nobody registered, and there is no enforcement point on the device or in the browser: the deployment targets are servers, clusters, pipelines and gateways.

agentless discoverynot in the model
MCP tool callslevel, top of scale
04

The clearest commercials in the category

Twenty dollars per agent per month, a free tier with no time limit, no prerequisite licence, and the traffic never leaves your network. Two of these rows go to Aembit and two are level — the strongest group on the page for them. Two things to ask on the call: log retention is twenty-four hours on the entry tiers and seven days on the paid team tier, and the posture conditions and content inspection are configured against platforms you license separately.

pricing unitlevel, per agent
platform costtheir row
Watch it happen

A verdict on the action itself, not on the door it came through.

Not a credential granted and a request forwarded — an inline decision on the write, with step-up authentication and human approval reaching the person it concerns in seconds, and an audit chain that runs request, approval, action performed.

action brokering · live product scene
What closes the gap

Keep the secretless access. Add the verdict on what the agent does with it.

Nothing about cryptographic attestation and just-in-time credentials conflicts with governing the action. Agen adds what a platform deciding reach cannot carry: every action judged against your policy at the moment it happens, in under 30ms, with five verdict types including step-up authentication, human approval and masking — a named accountable human resolved on each one, agentless discovery across five surfaces so the estate is not only the list you had, and enforcement on the device, in the browser, at the gateway and across cloud and SaaS.

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.

Sixteen capabilities publish from a worksheet of forty-one scored rows, on one 0–5 ladder applied to both columns. Rows were dropped rather than guessed: decision latency, because no vendor page publishes a figure; gateway enforcement, because it comes out level at the top of the scale and the MCP row already publishes that strength at tool-call granularity; shadow-agent discovery, because it is the same architectural fact the agentless-discovery row already scores and one design choice should not be counted twice; step-up authentication and human approval, neither documented in either direction; agent-to-agent authority chaining, undocumented in the product material; connector breadth, dropped on fairness because trust, credential and posture providers and governed connectors and tools do not measure the same thing; new-integration turnaround, unpublished; and our own audit-first rollout row, held pending internal confirmation. Ties and the two rows Aembit wins are printed rather than filtered.

Sources9 primary
Evidence ledger
  1. Aembit — general availability announcementTier AThe MCP Authorization Server implements the OAuth 2.1 authorization code flow defined in the MCP specification and runs as a managed capability. The MCP Identity Gateway “deploys as a Linux virtual machine in your environment… It validates tokens and enforces policy on every MCP request, then exchanges credentials on the agent's behalf so the agent never holds direct credentials for the MCP servers or the enterprise systems behind them.” Containerised and fully managed deployment options are described as on the roadmap.
  2. Aembit — Blended IdentityTier ABlended Identity “evaluates the identity of the AI agent and the human operating it together in a single policy decision, at request time”, and enforces per-user credential isolation so two users of the same agent receive credentials scoped to their own identities. “Every access event captures who the authenticated user was, which AI agent made the request, which resource was accessed, and what policy decision was made.” One user's access can be revoked without affecting other users or rotating a shared credential.
  3. Aembit — capability stackTier AThe capability stack comprises policy-based, just-in-time access so agents never hold standing credentials; conditional access factors including time of day, geography and runtime posture; “a kill switch that blocks a specific agent immediately via policy change”; structured audit logs that integrate with a SIEM; and a no-code deployment path through the host-based proxy, with CLI and SDK options also available.
  4. Aembit — pricingTier BPublished packaging for agentic AI: a free Starter tier with no time limit covering three AI agents, a single MCP Identity Gateway, five authorization service policies and twenty-four hours of event log retention; a Teams tier at $20 per agent per month scaling to five hundred agents with unlimited policies and seven days of retention; and an Enterprise tier with unlimited agents, conditional access, custom log retention and 24×7 support. No prerequisite platform tier is required.
  5. Aembit — Aembit Edge architectureTier AAembit Edge components are deployed within the customer's operational environments and enforce access policies by intercepting outbound requests from declared Client Workloads, gathering identity evidence such as Kubernetes service account tokens, cloud provider metadata and process information, requesting short-lived credentials from Aembit Cloud and injecting them into the original request before forwarding it. Supported deployment targets are Kubernetes, Amazon ECS, Linux and Windows virtual machines, AWS Lambda and GitHub, GitLab and Jenkins pipelines. “This design ensures that your sensitive workload data remains within your network boundaries and never passes through Aembit's infrastructure.”
  6. Aembit — Content SecurityTier AContent Security returns one of three verdicts on MCP content: allow, block, or transform, where “the inspection service returns modified content, such as content with sensitive data redacted, and Aembit forwards the transformed content in place of the original.” The available inspection service is CrowdStrike AI Detection and Response, which scans MCP tool listings, tool call inputs and tool call outputs in real time. “Content Security evaluates one MCP message at a time… Blocking one message doesn't block the messages that follow it.”
  7. Aembit — Access ConditionsTier AAccess Conditions gather and evaluate context on an access request: time-based conditions against configured schedules, GeoIP conditions against the source address, and security posture conditions that call third-party platforms including Wiz and CrowdStrike. “If all Access Conditions pass, Aembit proceeds to credential issuance. If any condition fails, Aembit immediately denies access.” The documentation describes the result as “MFA-like protection for workloads by requiring both identity and context verification before granting access”.
  8. Aembit — IAM for Agentic AITier AThe MCP Authorization Server integrates with identity providers the organisation already uses, including Okta, Azure AD and Google, through OIDC and SAML. “Aembit does not replace your workforce identity provider. It consumes data from your existing IdP and binds it to the agent's operating context, without requiring new identity infrastructure.”
  9. Aembit — stack mapTier BAembit publishes a map of thirty-four named integration vendors across API security, identity governance, privileged access management, secrets managers, user IAM and workload identity federation, positioning the platform as replacing secrets managers and non-human identity governance tools while integrating with and running alongside workforce IAM and privileged access management.

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 Aembit?
It is an identity and access management platform for workloads and AI agents. A control plane holds the access policies and makes the decisions; a data plane deployed inside the customer's own environment — as a Kubernetes sidecar, an ECS task, a virtual machine, a Lambda layer or a CI/CD runner — intercepts a workload's outbound request, attests the identity of the process making it, obtains a short-lived credential and injects it into the request before forwarding. For AI agents it adds an authorization server implementing the OAuth 2.1 flow from the MCP specification and an identity gateway that validates tokens and enforces policy on every MCP request. Its access model, Blended Identity, evaluates the agent's identity and the human operating it together in a single decision.
Where does Aembit score better than or level with Agen.co?
Two rows of sixteen go to Aembit outright and four more are level — the most competitive table we publish. Total platform cost is theirs: a free tier with no time limit and a posted price of twenty dollars per agent per month with no prerequisite licence, against the 4 we score on that row everywhere. Ecosystem breadth is theirs too, on a published stack map of thirty-four integration vendors plus a Terraform provider and Helm charts, against our deliberately focused platform. Level with us on the pricing unit, because per governed agent is the same unit we bill on; on delegated on-behalf-of access, where credentials are exchanged and isolated per user; on MCP tool governance; and on deployment model and data residency, where workload traffic never leaves the customer's network.
Does Aembit enforce policy at runtime?
Yes, and it is worth being exact about what the enforcement is about. Policy is evaluated on every request that passes through an Edge component — not once at login — and conditions on time of day, geographic location and third-party endpoint posture are checked on each one. If all of them pass, a short-lived credential is issued and injected; if any fails, access is denied immediately. The distinction the table carries is that the decision is about whether a credential may be held for a resource, rather than about the specific action performed with it. Once the request is forwarded, which record the agent touches and what comes back is decided inside the target system. The exception is MCP traffic with content inspection configured, where tool call inputs and outputs are examined and can be returned redacted.
How does Aembit identify the human behind an agent?
Through Blended Identity, and it is the strongest version of this we have scored in the category. The agent's non-human identity and the identity of the human operating it are evaluated together in a single policy decision at request time, so a rule can say that engineers may use a given assistant to reach the issue tracker while only the security team may use it to reach the vulnerability scanner. Credentials are isolated per user, so two people driving the same agent get access scoped to themselves and one can be revoked without touching the other, and every access event records both identities alongside the resource and the decision. The caveat is which human it is: the model blends in the person operating the agent, so an agent running autonomously on a schedule, with nobody at the keyboard, has no human to blend.
Can Aembit discover AI agents it was not told about?
Not from what is published. Coverage begins with a Client Workload that somebody declared and an Edge component that somebody deployed, and no discovery capability appears in the product material — an agent that was never onboarded has no policy to evaluate and no path through the platform. That is a consequence of the architecture rather than an omission: it is precisely because workloads are declared and attested that traffic can stay inside the customer's own network, which is a row Aembit ties at the top of the scale. Agen approaches it from the other end, with agentless discovery across five surfaces and no SDK or self-registration, so the estate under governance is not limited to the list somebody already had.
How is Aembit priced?
It is published, which is unusual in this category and is scored that way. There is a free Starter tier with no time limit covering three AI agents, a single MCP identity gateway and twenty-four hours of log retention; a Teams tier at twenty dollars per agent per month scaling to five hundred agents with seven days of retention; and an Enterprise tier with unlimited agents, conditional access and custom retention. No prerequisite platform tier is required. Because the unit is per agent, the pricing-unit row ties with us at the top of the scale, and the total-cost row goes to Aembit. Worth asking about on the call: log retention on the lower tiers is short, and the posture conditions and content inspection depend on third-party platforms licensed separately.
Can Agen.co run alongside Aembit?
Yes, and the two sit at different points, so it is a natural shape. Aembit keeps doing what it is good at: attesting workloads cryptographically, minting short-lived credentials per request so no standing secrets exist, carrying the human operating an agent into the decision, and keeping the traffic inside your network. Agen governs what those agents do next — every action judged against your policy in under 30ms, with five verdict types including step-up authentication, human approval and masking, a named accountable human resolved per action, agentless discovery across five surfaces to find the agents nobody registered, and enforcement on the device, in the browser, at the gateway and across cloud and SaaS. Nothing you already run has to be removed.
What is the difference between issuing a credential and governing an action?
Issuing a credential answers which systems an agent may reach and on whose behalf; governing an action answers whether the specific thing it just did was permitted. The first is a decision about scope, and it can be made before the request leaves — deny it, scope it tighter, expire it in five minutes. The second is a decision about a write, an export or a delete at the moment it is attempted, and it can depend on things only the action knows: what is in the payload, how many rows there are, whose data is in the response, whether a person ought to approve it first. Both are worth having, and a short-lived scoped credential is a much better starting point than a shared key in an environment variable. Only one of the two is available while the action is still in flight.

See the row that decides it.

Bring an agent that already holds a short-lived credential and reaches a system through a policy you wrote. We will show you a verdict on the write it makes with that credential — resolved to the person the write was for, with the option to mask, step up or hold it — in a working environment, in under a day.

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