Why Your Company Needs a Context Graph (and How to Build It) — Gil Feig, Merge
The talk outlines the necessity of a 'context graph'—a comprehensive 'company brain'—for building production-grade AI agents. The core argument is that simple live API lookups (like those from MCP servers) fail when attempting semantic queries across vast, historical datasets (e.g., 'Which customers were upset last year?'). The solution requires a multi-layered architecture that combines live data lookups with synced, cached data stored in a vector database. This data flow is managed by a Router and a Summarizer, and crucially, must incorporate company-specific 'skills' and 'memories' to ensure the agent follows defined business processes. Traceability and provenance are mandatory for all data used in business-critical decisions.
Key takeaways
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Context is more than tools; it's data and process.
3:47
A context graph must integrate third-party data (e.g., NetSuite, Jira), structured context, memories, and company-defined skills/processes, not just API connections.
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Live lookups are insufficient for semantic queries.
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Live API lookups are effective for simple, single-action lookups (e.g., checking a specific customer's status in Stripe), but fail when the query requires searching across large datasets or complex semantic analysis.
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The architecture requires a Router and a Summarizer.
10:17
A Router directs the incoming prompt to the correct handler, while the Summarizer aggregates and synthesizes data retrieved from both synced and live context sources before sending the final answer to the agent.
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Traceability and Provenance are non-negotiable.
Every answer must track its data source, fetch time, associated ID, and what conditions (e.g., age, invalidation) might render the data outdated, especially for business-critical decisions.