Every action earns its authority.
Identity, purpose, policy, and payload are evaluated together at the moment of execution.
Runtime control for AI agents
Track sits between AI agents and the systems they act on, deciding what can run, binding approvals to the exact payload, and signing the proof.
Built for the systems your agents already use:
The control gap
One platform
From discovery to decision to proof, Track gives your team one calm place to govern AI agents.
See every agent, identity, MCP server, and reachable tool, with the source behind every claim.
Explore discovery → 02Allow, deny, restrict, or escalate from identity, purpose, environment, policy, and exact payload.
Explore decisions → 03Bind request, decision, approval, and execution into a signed receipt anyone can verify offline.
Explore receipts →Built around the action
Track makes four things true at runtime, without teaching the agent new behavior or asking another model to judge it.
Identity, purpose, policy, and payload are evaluated together at the moment of execution.
Deterministic policy adds control without adding another model round-trip.
What runs must cryptographically match what a person or policy approved.
Signed receipts stay verifiable with Track offline, and with your data and keys in your environment.
One decision point
The agent keeps making its normal call. Track adds the control your team needs without teaching the agent new behavior.
See how it worksA tool or API call arrives with identity, purpose, and payload.
Policy evaluates the action in its exact runtime context.
Only the allowed, cryptographically bound payload gets through.
A signed record captures what was requested, approved, and run.
Proof that travels
Every Track receipt binds the exact request, decision, approval, policy version, and execution result into portable evidence.
See receipt anatomyStart with one real workflow
We’ll map it, add a decision point, and produce receipts in your environment.
Design a pilot