Introduction to AI Agent Governance¶
Native policy, host controls, identity, and trust
Why governance¶
- Agent actions can create external side effects.
- Prompt instructions are not an authorization boundary.
- Policy, identity, isolation, audit, and reliability solve different problems.
Native ACS policy¶
An ACS manifest declares:
- policy engines and bundles
- intervention-point bindings
- tool catalogs
- transforms and evidence
- approval
- limits
Runtime model¶
from agent_control_specification import AgentControl, HostSession
runtime = AgentControl.from_path("policies/manifest.yaml")
session = HostSession(
runtime,
agent_id="agent-1",
session_id="session-1",
)
Tool mediation¶
No side effect occurs before the pre-intervention evaluation.
Result contract¶
PolicyEvaluation carries:
- verdict and reason code
- transform and evidence
- input and enforced identities
- approval metadata
- restricted audit payload
Lab 1¶
Run labs/lab1_first_policy.py.
- Inspect the manifest binding.
- Inspect the Rego policy.
- Run the scenarios.
- Add one deny condition.
Host controls¶
Policy evaluation does not replace:
- sandbox resource and network controls
- framework lifecycle ordering
- circuit breakers and SLOs
- identity and trust
AgentMesh¶
AgentMesh owns:
- cryptographic identity
- trust and reputation
- capability grants
- cross-agent transport
Lab 2¶
Complete labs/lab2_multi_agent_trust.py.
- Create identities.
- Attempt a trust handshake.
- Build trust.
- Revoke and verify denial.
Fail closed¶
- Unknown tools can be denied by manifest contract.
- Runtime and approval errors deny.
- Action-identity mismatch denies.
- Public errors remain sanitized.
Production checklist¶
- Lint and replay manifests.
- Test every mediated framework path.
- Test sandbox isolation.
- Test approval and runtime failures.
- Restrict audit access.
- Monitor SLOs and incident signals.
Next steps¶
docs/quickstart.mddocs/tutorials/03-framework-integrations.mddocs/tutorials/55-agent-control-specification.mddocs/workshop/lab-guide.md