agentic-rag

Agentic RAG uses an autonomous agent that evaluates queries and determines whether retrieval adds value before initiating searches. The agent reasons through THINK/ACT/OBSERVE loops, deciding dynamically when and what to search based on the conversation context and knowledge gaps.

Yes. Agentic RAG supports multi-turn dialogues where the agent autonomously decides on-demand when to search for factual information within an ongoing conversation, enabling adaptive knowledge-base exploration without fixed retrieval triggers.

Standard RAG retrieves on every query; agentic RAG lets the agent decide if retrieval is needed. This multi-step reasoning approach reduces unnecessary searches, refines results iteratively, and handles complex queries that require reasoning before or between retrievals.

Agentic RAG requires modular tool design with configurable iteration limits and an agent behavior policy. You define how many reasoning steps the agent can take and set policies that guide when the agent should search, observe results, or conclude.

Yes. Agentic RAG is built for conversational AI assistants that need to autonomously decide when factual retrieval enhances responses. It integrates tool-use capabilities to orchestrate retrieval within natural dialogue flows.

The agent responds using existing knowledge without searching. Agentic RAG's autonomous decision-making avoids unnecessary retrieval calls, reducing latency and computational cost while maintaining answer quality for queries the model can handle independently.