AgentTrust for the enterprise

Control and validate every agent your teams build

Across a large organization, agents get built everywhere — by data teams, platform teams, individual product squads, and third-party SaaS vendors. They use different frameworks and run in different clouds, but they all reach for the same high-impact systems. AgentTrust puts a single, enforceable control point in front of all of them.

Where AgentTrust sits

No matter where an agent is built or what framework it uses, it must pass through the trust layer before it can act — and every attempt lands in one audit trail.

Where agents are built

LangChain / LangGraph

OpenAI Assistants

CrewAI / AutoGen

Internal Python agents

Vendor SaaS copilots

Different teams, frameworks, and vendors — no shared runtime.

→↓

AgentTrust layer

Verify agent identity

Evaluate org policies

Allow · flag · deny

One control point for every agent

→↓

Systems they act on

Payments (Stripe, SAP)

Databases & warehouses

Email / Slack / CRM

Internal APIs & tools

Cloud & shell commands

High-impact systems that need guardrails before an agent touches them.

Audit rail: every decision from every agent is written to one immutable log — the system of record for security, risk, and compliance teams.

The problem

Agents ship faster than governance

The gap most enterprises hit once agents move past prototypes.

No shared identity
Each agent authenticates to downstream systems with its own service credentials — there is no consistent notion of 'which agent did this'.
Rules live in prompts
Guardrails are buried in system prompts and easily bypassed, drift between teams, and can't be audited.
No central kill switch
When an agent misbehaves, there is no single place to suspend it or tighten what it's allowed to do.
No evidence for auditors
Risk and compliance can't answer 'what did the agents do, and what was blocked?' without scraping logs from every team.

The control model

Three decisions, one policy language

Every action an agent attempts resolves to one of three outcomes. The same model works for payments, data, messages, and infrastructure — you only change the policies.

Allow

The action matches no deny or flag rule. It proceeds and is still logged.

Flag

The action is permitted but recorded for review, or routed to a human for approval before it runs.

Deny

The action is blocked before it reaches the target system. The agent receives a 403 and a reason.

Strictest verdict wins. When several policies match one action, deny beats flag beats allow — so a single deny rule is always enough to stop an action, no matter what else is configured.

Use cases

How it works across the organization

Four representative agents from different teams, the risk each one introduces, and the exact policies that keep them in bounds.

Finance operations

A procurement agent that pays invoices

Situation

An agent reads incoming invoices and issues payments through the Stripe/SAP API so the AP team no longer processes them by hand.

Risk without a trust layer

A hallucinated or manipulated invoice triggers a large, irreversible payment to an unknown vendor.

Outcome with AgentTrust

Payments under $2,500 to known vendors clear automatically; larger or first-time payments are denied or routed to a human. Finance keeps velocity without unbounded exposure.

Policies

json

{
  "name": "Cap autonomous payments",
  "effect": "deny",
  "action": "payment.send",
  "conditions": [
    { "field": "amount", "op": "gt", "value": 2500 }
  ]
}
// Second rule: flag anything to a new payee for human review
{
  "name": "Review new payees",
  "effect": "flag",
  "action": "payment.send",
  "conditions": [
    { "field": "payee_known", "op": "eq", "value": false }
  ]
}

Data & analytics

A support copilot that queries customer data

Situation

A customer-support agent runs read queries against the warehouse to answer questions about accounts and usage.

Risk without a trust layer

The agent runs a bulk export or touches PII columns it was never meant to see, creating a data-exfiltration path.

Outcome with AgentTrust

The agent can answer questions but cannot mutate data or pull mass exports. Every query it attempts is logged with the requesting agent's identity.

Policies

json

{
  "name": "Read-only warehouse access",
  "effect": "deny",
  "action": "db.*",
  "resource": "warehouse",
  "conditions": [
    { "field": "operation", "op": "in",
      "value": ["insert", "update", "delete", "drop"] }
  ]
}
// Block wide exports
{
  "name": "No bulk export",
  "effect": "deny",
  "action": "db.query",
  "conditions": [
    { "field": "row_limit", "op": "gt", "value": 1000 }
  ]
}

Sales & marketing

An outreach agent that emails customers

Situation

A growth agent drafts and sends outbound emails and Slack messages to prospects and existing customers.

Risk without a trust layer

The agent emails a suppressed contact, sends during a blackout window, or blasts a segment far larger than intended.

Outcome with AgentTrust

Suppressed contacts are never emailed, and large campaigns wait for a human to approve the flagged action before they go out.

Policies

json

{
  "name": "Respect suppression list",
  "effect": "deny",
  "action": "email.send",
  "conditions": [
    { "field": "recipient_suppressed", "op": "eq", "value": true }
  ]
}
// Flag large sends for approval
{
  "name": "Approve mass sends",
  "effect": "flag",
  "action": "email.send",
  "conditions": [
    { "field": "recipient_count", "op": "gt", "value": 50 }
  ]
}

Platform & DevOps

An SRE agent that runs remediation commands

Situation

An incident-response agent executes shell and cloud commands to restart services and scale infrastructure during outages.

Risk without a trust layer

A misdiagnosis leads the agent to delete a database, terminate production instances, or change IAM policy.

Outcome with AgentTrust

Destructive commands are blocked outright; production-scoped changes are flagged for an on-call engineer, so the agent can help without being able to cause an outage.

Policies

json

{
  "name": "Block destructive infra actions",
  "effect": "deny",
  "action": "command.exec",
  "conditions": [
    { "field": "command", "op": "contains", "value": "rm -rf" }
  ]
}
// Require review for production-scope changes
{
  "name": "Gate production changes",
  "effect": "flag",
  "action": "cloud.*",
  "conditions": [
    { "field": "environment", "op": "eq", "value": "production" }
  ]
}

Rollout

Adopt in three phases — without blocking teams

You don't flip enforcement on day one. AgentTrust is designed to earn trust before it starts blocking.

1
Observe
Point agents at AgentTrust with all policies in allow/log mode. You immediately get an inventory of every agent and a full record of what they attempt — with zero disruption.
2
Flag
Turn high-risk patterns into flag rules. Actions still succeed, but you build a review queue and confirm the policies match real behavior before enforcing.
3
Enforce
Promote validated flag rules to deny. Now the highest-impact actions are blocked at the edge, and everything else keeps flowing — with the audit trail intact.

Roles

Who owns what

AgentTrust gives each stakeholder the surface they need without stepping on the others.

Role	What they do in AgentTrust
Agent developers	Register their agent, get an API key, and add one evaluate call before guarded actions.
Security & risk	Author and own the deny/flag policies; review flagged actions; suspend misbehaving agents.
Platform team	Run the deployment, manage the data store, and set org-wide default policies.
Compliance & audit	Use the immutable log as the system of record for what agents attempted and what was blocked.

One audit trail for the whole agent fleet

Every evaluation — allowed, flagged, or denied — is written with the agent's identity, the action, the matched policies, the reason, and a request ID. That single log answers the questions auditors and incident responders actually ask: which agent tried to do what, when, and what did we stop?

See the technical architecture →Open the console →