Govern, evaluate, route, approve, audit, and benchmark enterprise AI agents before production. Built as a deterministic control-plane reference implementation with live connectors and live providers disabled by design.
Capture business intent, owner, domain, outcome, data, and autonomy expectation.
Classify suitability, risk, data readiness, controls, approvals, and production gates.
Apply policy-as-code to tools, data, environments, model routing, identity, and secrets.
Record traces, audit events, decisions, blocked actions, evidence, and readiness reports.
Use benchmark, release-evidence, deployment, public-site, and launch-candidate checks.
Follow the procurement-agent control-plane journey from intent to readiness report.
Review the unified capability map, API surface, release status, and demo flow.
Inspect deterministic endpoint groups and OpenAPI-lite metadata.
View reusable scenarios, benchmark suites, scoring posture, and sample runs.
Review validation snapshot, public proof bundle, and demo recording readiness.
Check GitHub Pages finalization, publication sequence, evidence, and social launch copy.
Demonstrates an end-to-end control-plane flow: PO, invoice, challan, vendor consistency, governance, policy, runtime, sandbox tool execution, traceability, and readiness reporting.
The launch candidate does not call live providers, execute live connectors, store raw secrets, provision cloud infrastructure, or implement production IAM. It models the control plane before production.
Further feature development should pause until the repository is published, the demo is recorded, and external feedback is collected.