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 - 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 - 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 - 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 - AI Governance Platform becomes the durable system of record for governance state, evidence, decisions, and audit context.
OSS capabilities used