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Manual RAG vs. Agentic Context Retrieval: Building for Multi-Source Enterprise Data

Why naive 'embed it and RAG it' fails for real multi-source enterprise data, and the 3-layer Ingestion/Retrieval/Generation architecture (grounded in the open-source Airweave framework) needed to actually solve it.

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▶📚 Prerequisites(2)

🎓 Learning objectives

  • •Explain why a query spanning multiple data sources (Linear, Calendar, Gmail, Slack) breaks naive single-source RAG
  • •Describe the 3 layers of an agentic context retrieval system: Ingestion, Retrieval, and Generation
  • •Identify the specific sub-problems each layer must solve (auth, per-source processing, incremental refresh, query expansion, source routing, hybrid search, authorization filtering, recency weighting)
  • •Explain why detecting genuine content changes (vs. permission-only changes) is harder than simple timestamp comparison

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📂 Subtopics

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The Manual RAG Pipeline: 'Embed It and RAG It' — and Where It Works

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Agentic Context Engineering: The Multi-Source Query Problem

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Key Differences: When Agentic Retrieval Beats Manual RAG, and When It Doesn't

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Infrastructure Requirements: Ingestion, Retrieval, and Generation Layers (Airweave)

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