As agents move into operational workflows, they need more than access to relevant documents; they need a trusted understanding of relationships, dependencies, policies, ownership and business impact.

This whitepaper introduces a blueprint-driven knowledge graph approach built around a Minimum Viable Ontology and Minimum Viable Graph. It explains how enterprises can define approved entities, relationships and constraints before extraction begins, enabling agents to reason over validated domain structures instead of inferring context from fragmented text.

The paper provides a practical architecture pattern, governance lifecycle, real-world HCLTech use cases and adoption roadmap to help organizations create queryable, auditable and enterprise-ready knowledge graphs that strengthen Agentic AI reasoning, decision support and operational control.