Building a Governed Agentic AI Platform
India-based enterprise
A Retrieval-Augmented Generation platform on Azure AI Foundry took an India-based enterprise from experimental AI assistants to a governed Agentic AI foundation.
- Every agent response grounded in and traceable to approved sources
- Source-citedEvery agent response grounded in and traceable to approved sources
- Agents coordinating retrieval, drafting, approval and system updates
- Multi-stepAgents coordinating retrieval, drafting, approval and system updates
The challenge
What was going wrong
An India-based enterprise sought to adopt Agentic AI to improve access to organisational knowledge and automate multi-step business activities — moving beyond conventional chatbots toward agents capable of understanding context, retrieving trusted information, coordinating activities and interacting with enterprise systems. Existing search tools returned documents rather than usable answers, while initial generative AI experiments lacked grounding, control and auditability, across information scattered in document repositories, databases, internal portals, SOPs, policy documents and transactional systems.
Our approach
What we built
- 01
Established a governed enterprise knowledge pipeline covering source discovery, content classification, data preparation, document chunking, metadata enrichment, vector indexing, semantic search, access-control inheritance and source citation.
- 02
Built RAG-based intelligence so agents retrieve information from approved enterprise sources before generating responses, improving answer relevance, contextual accuracy, source traceability and information governance.
- 03
Designed agentic orchestration for controlled multi-step activities: retrieving information, comparing policies, preparing summaries, generating drafts, initiating workflows, calling approved APIs, requesting human approvals and creating audit records.
- 04
Established AI Foundry governance covering model-selection criteria, prompt management, evaluation datasets, safety filters, deployment separation, monitoring, logging, cost tracking and agent lifecycle governance.
The outcome
What changed
- Faster access to enterprise knowledge and improved response grounding.
- Reduced dependence on manual information search and better control over AI-generated outputs.
- Controlled execution of multi-step activities and a reusable architecture for additional agents.
- Improved auditability and operational visibility, and a scalable foundation for enterprise-wide Agentic AI.
