AgentOps Mesh is an open-source control plane that decides whether an AI agent should be allowed to exist, use data, call tools, and move toward production — with deterministic policies, not AI judging AI.
Capture business intent, owner, domain, and autonomy expectation.
Classify suitability, risk, data readiness, and approvals.
Apply policy-as-code to tools, data, models, and identity.
Record traces, audit events, blocked actions, and evidence.
Benchmark, release evidence, and launch readiness checks.
Deterministic rules for tool scope, cost ceilings, data access, and approvals. Same input, same decision — every time.
Intake → Suitability → Data → Evaluation → Policy → Approval → Runtime → Deployment → Launch. No gate skipped.
Tamper-evident logs of every agent decision: perception, reasoning, action, policy checks, and approval provenance.
Per-session cost ceilings, per-agent budgets, alert thresholds, and an automatic kill switch at 100%.
Approval gates for sensitive actions. SLA timers, escalation rules, and full approval audit trail.
AXON agents submit directly to Mesh for governance. Write in AXON, govern in Mesh — one pipeline. See how →
Follow a real control-plane journey: from business intent to governance classification, policy decision, sandbox execution, audit trail, and readiness report.
Governance that's right 90% of the time isn't governance — it's a suggestion. AgentOps Mesh uses policy-as-code, not AI-as-judge, for the decisions that matter. Every policy evaluation produces the same result every time. That's what an operator can defend in production.