🚀

AI Agent Governance for Deployments

Control AI agent access to deployment pipelines. Require CI pass before deploys, prevent untested code from reaching production.

⚠

The Risk

AI coding agents can commit code, trigger builds, and initiate deployments. Without governance, an overeager agent could push broken code directly to production:

✗ Without HaltState

Coding agent "fixes" a bug and deploys directly to production, bypassing CI. The fix breaks authentication. 50,000 users locked out for 2 hours. Incident costs $150K in customer credits.

✓ With HaltState

Agent attempts production deploy. HaltState checks CI status - tests haven't run. Deployment blocked. Agent told to wait for CI. Tests run, reveal the auth bug. Agent fixes it properly.

📝

The Policy

Enforce deployment gates that apply to both humans and AI agents.

"Deny deploy.production unless ci_status = passed"

With additional safeguards:

{ "action": "deploy.production", "effect": "deny", "condition": "NOT (params.ci_status == 'passed' AND params.tests_passed == true)", "reason": "Production deploys require passing CI and tests" }
🔄

The Flow

What happens when an agent attempts to deploy without CI:

Agent calls
deploy.production
→
HaltState
intercepts
→
CI status:
not_run
→
DENIED
→
Agent triggers
CI pipeline
→
CI passes:
retry allowed

Agent learns to wait for CI. Production stays stable.

📜

The Evidence

Complete deployment audit trail for compliance and debugging:

{ "action": "deploy.production", "agent_id": "coding-agent-v2", "decision": "DENIED", "params": { "commit_sha": "a1b2c3d4", "branch": "feature/auth-fix", "ci_status": "not_run", "tests_passed": false }, "reason": "Production deploys require passing CI and tests", "subsequent_action": { "action": "deploy.production", "decision": "ALLOWED", "ci_status": "passed", "tests_passed": true } }

Full history of deployment attempts, denials, and successful deploys.

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