Agentic Search
Agentic search is an information retrieval approach where autonomous agents actively explore file systems or knowledge bases rather than relying on pre-indexed query-matching systems. Instead of retrieving results from a static database, the agent navigates through documents iteratively, assessing relevance and deciding which paths to explore next based on discovered content. This treats retrieval as an interactive process of navigation and discovery rather than a lookup operation.
Distinction from RAG
Traditional retrieval-augmented generation (RAG) systems depend on embedding-based similarity matching against pre-computed indices. An agentic search approach bypasses this static indexing phase. The agent reads documents dynamically, follows logical connections between files, and uses reasoning to determine which new areas to investigate. This can be more flexible for scenarios where document relationships are complex or where the structure of the knowledge base is irregular.
Implementation
The fs-explorer project demonstrates agentic search applied to file system exploration. An agent traverses directories, reads file contents, and makes decisions about which branches to follow based on semantic understanding of what it discovers. The agent can adjust its search strategy in real-time, prioritizing promising leads and abandoning unproductive paths without needing to query a pre-built index.
Source Notes
- 2026-04-14: “But OpenClaw is expensive…”
- 2026-04-07: Chroma Context 1 Self Editing Search Agent for Efficient RAG · ▶ source
- 2026-04-10: Meta Muse Spark Features Performance and Strategic Shift to Proprietar · ▶ source
- 2026-04-13: MiniMax M27 Open Source LLM Rivaling Opus 46 with Agent Capabilities · ▶ source
- 2026-04-17: Anthropic Claude Opus 47 Performance Gains Safety Limits Strategic Rel · ▶ source
- 2026-04-25: Claude Code · ▶ source
- 2026-04-28: Apple
- 2026-04-29: Optimizing LLM Agent · ▶ source