What is Agentic Retrieval
Agentic Retrieval - AI agents that autonomously plan and execute multi-step retrieval strategies
Agentic retrieval is a retrieval paradigm where an AI agent dynamically decides how to search, what tools to use, and when to refine its queries based on intermediate results. Unlike static retrieval pipelines with fixed steps, agentic retrieval adapts its strategy in real time — breaking complex information needs into sub-queries, evaluating partial results, and iterating until the best answer is found.
How It Works
An agentic retrieval system gives an LLM access to retrieval tools (vector search, keyword search, filters, aggregations) and lets it plan a multi-step search strategy. The agent formulates an initial query, evaluates the results, decides whether they are sufficient, and if not, reformulates the query or tries a different approach. This loop continues until the agent determines it has found the relevant information.
Technical Details
Agentic retrieval combines tool-calling LLMs (like GPT-4 or Claude) with retrieval APIs exposed as callable functions. The agent receives a schema describing available search tools, their parameters, and return types. ReAct-style prompting enables the agent to reason about which tools to call and in what order. Results are accumulated in a working memory that informs subsequent tool calls.
Best Practices
- Define clear tool schemas with parameter descriptions for the agent
- Set maximum iteration limits to prevent infinite search loops
- Include diverse retrieval tools (vector, keyword, filter, aggregate) for flexibility
- Log agent reasoning traces for debugging and optimization
- Use evaluation benchmarks to measure agentic vs. static retrieval quality