Agentic Retrieval

Agentic retrieval studies how AI agents find and use external information through successive search, inspection, and reasoning steps rather than relying on a single ranked result list. Common approaches combine lexical or dense search with query reformulation, document navigation, graphs or structured memory, and tools for checking evidence and deciding when to stop. The methods are tested in settings such as multi-hop question answering, deep research, long-term conversational memory, code repositories, and multimodal search, with training increasingly drawing on agent trajectories and reinforcement learning. Agents can improve evidence finding on complex tasks, but extra tool calls bring latency and cost, and better retrieval does not guarantee correct reasoning or faithful answers.