Observed Signal · Jun 23, 2026 · Technical Article · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Multi-agent document-search copilot: one strategy per query
This technical blog post (Part 1 of 2) describes building a multi-agent chat copilot for document search and the engineering changes that fixed poor ranking quality. The author explains that v1 ran two retrieval lanes (structured metadata + semantic content) in parallel, merged their hits, and reranked the union — producing plausible-but-incorrect rankings. v2 replaces that with a single structured-output router call (Bedrock) that returns a typed plan and selects exactly one retrieval strategy per query: MetadataOnly, ContentOnly, Hybrid, NoMatch, or NeedsClarification. Reranking uses Cohere over content; metadata rows are treated as unscored results. The post also details a deterministic fallback to a non-LLM router and previews Part 2, which will cover the adaptive Hybrid path (selectivity-based) and permission gating.
Practical engineering guidance for LLM-based retrieval and reranking systems that is useful to teams building conversational/document copilot features, but it is not a platform policy or major product launch.
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Key Takeaways & Evidence Grounding
- v1 fused two retrieval lanes (metadata and semantic content) and reranked their union, causing poor relevance ordering.
- v2 collapses routing into a single structured-output Bedrock call that returns route, rewritten query, strategy, and filters.
- v2 enforces one retrieval strategy per query: MetadataOnly, ContentOnly, Hybrid, NoMatch, or NeedsClarification.
- Content reranking uses Cohere for content passages; metadata results pass through unscored.
- A deterministic fallback to a non-LLM router is used if the structured router call fails.
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