Building a Foundation of Intent Data to Power Agentic Automation

Network teams pursuing agentic automation face a key obstacle: existing operational data is often fragmented and lacks the relationship context needed for AI agents to safely answer cross-system questions or act on critical infrastructure. The proposed solution is an AI-ready intent-data foundation that captures not only devices and inventory but also the intended design, topology, services, customers, contracts, and dependencies in a unified, relationship-rich model. Such a foundation should support agent-friendly querying through interfaces such as GraphQL and MCP, remain current through governed changes and reconciliation against discovered network state, and reflect each organization’s specific environment rather than a generic vendor schema. Infrahub is presented as a graph-based platform for building this foundation by integrating existing tools and data sources into a governed knowledge graph, while Infrahub Skills uses AI assistants to help engineers create schemas, populate data, build automation and validation logic, analyze data, detect drift, and audit implementations. The recommended approach is to begin with a narrowly scoped production proof of concept, establish reliable intent modeling and reconciliation, and expand agentic automation as trust and capabilities grow.

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