Building retrieval harness for enterprise agents
This technical walkthrough details the architecture of an enterprise agentic retrieval harness, moving beyond traditional Retrieval Augmented Generation (RAG) systems. The core argument is that effective data access requires giving agents a comprehensive set of tools—including local file system traversal (GP), semantic search, keyword search, and multimodal analysis—rather than relying on a single indexing method. Key focus areas include managing complex document parsing, ensuring multi-tenancy via custom metadata, and optimizing vector storage for scale and cost.
Key takeaways
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Harness over RAG
2:00
The most capable approach is not to select a single retrieval method (e.g., pure semantic search or pure GP access) but to build an agentic harness that allows the agent to decide how to combine multiple tools for data exploration and verification.
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The Role of File Primitives
4:20
For document-driven workflows, critical primitives include: (1) Retrieval (hybrid search), (2) Directory listing/hierarchy traversal, (3) Grep for exact text matching, (4) Chunk-by-chunk analysis, and (5) Multimodal processing via localized screenshots.
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Enterprise Scaling Challenges
6:45
Scaling to multi-tenant or million-file situations requires specialized indexing pipelines that manage data freshness, security, and permissioning. Using custom metadata attached during the parsing process is crucial for filtering access at the vector storage layer.