How to Structure Context to Make Your Agents Smarter
summary
Claim: agents fail from fractured context not bad models, so add a structured knowledge layer to retrieval. Demonstrated architecture pairs vector RAG with a knowledge graph augmented context using graph embeddings (fastRP), LangChain/Semantic Kernel integrations, and Neo4j; an ablation shows 37→54→91% accuracy when adding a graph; industry stats cited: 95% of pilots show no measurable P&L and Gartner predicts 40% cancellations by 2027. Live demo (Versed app, GitHub, command uvx create context graph) produces an 83% confidence rejection and visual decision traces.
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chapters (12)
- 0:00Introduction
- 4:40Fractured agent context
- 8:10Better models vs broken context
- 10:58What is context engineering
- 12:24Context engineering techniques
- 15:53Knowledge graphs for agents
- 18:23Research on knowledge graphs
- 20:31From knowledge to context graphs
- 21:53Agents need memory
- 24:01Live demo: context graph
- 27:34Demo takeaways
- 28:15Resources
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