Understanding Context Engineering for Agentic Systems
summary
Practical framework that names tools and outcomes: uses Redis Specter with LangChain and LangGraph integrations, contrasts a simple RAG pipeline against a context-engineered system that pulls user and order data to auto-infer refunds, and reports that decomposition and tool calling actually reduce errors. Emphasizes prune context aggressively, decompose tasks with specialized subagents, and treat AI as software first with cron refreshes, schema-driven memory, and measurable reductions in noisy retrievals.
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chapters (10)
- 0:00Context engineering intro
- 0:56Scaling AI systems lessons
- 2:39LLMs and agentic rise
- 4:25RAG solved but new issues
- 5:53Pre-RAG challenges
- 7:18Unstructured data fails
- 9:03Agent decomposition advances
- 10:27RAG vs context engineering demo
- 12:13Context engineering tips
- 13:39Summary and future
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