Story 05 · Platform Engineer
Integrate governance into an existing AI platform
Add a governance control plane to the systems you already run through public REST and MCP interfaces, without moving orchestration into BHANUJ - AIGP (AI Governance Platform).
The situation
A platform team already owns application orchestration, model serving, and deployment. It needs a consistent way to send governed facts into BHANUJ - AIGP (AI Governance Platform), request evaluations, and retrieve decision and audit records.
The question
How can we add governance without replacing our existing platform?
The governance workflow
From question to governed outcome
- 01
Connect through the public interface
Choose REST for conventional service integration or MCP for an MCP-aware client; both are adapters over the same control plane.
- 02
Submit governed observations and work
Report runtime asset evidence, submit evaluations, and create or manage jobs through tenant-scoped, authorized operations.
- 03
Follow job execution
BHANUJ - AIGP (AI Governance Platform) executes durable governance jobs and makes status, results, and failures available to the platform.
- 04
Retrieve the governance outcome
Read evaluations, governance decisions, explanations, evidence, and lineage through the same public control-plane boundary.
- 05
Review the audit trail
Use correlation and audit records to account for controlled operations across the integration.
In Studio
The screens behind the story
REST exposes the governed operations a platform can automate.
MCP-aware clients can use the same governed control plane.
Durable jobs provide visible status and a governed execution record.
The outcome
The existing platform keeps runtime ownership while BHANUJ - AIGP (AI Governance Platform) becomes the durable system of record for governance state, evidence, decisions, and audit context.
OSS capabilities used