Retrieval-Augmented Generation Agentic RAG

Retrieval-Augmented Generation Agentic RAG is a next-gen architecture that marries classic RAG pipelines with autonomous decision-making agents. Instead of a single prompt-retrieve-generate loop, an agentic RAG system plans multistep actions: it clarifies user intent, queries vector databases, ranks sources, drafts an answer, tests factual consistency, and iterates until confidence thresholds are met. The agent holds long-term context in memory, calls external tools (search APIs, calculators, code interpreters), and applies guardrails for security and cost. By treating each subtask—formulating search queries, picking embeddings, citing evidence, rewriting for tone—as a discrete objective, the agent can self-correct, learn from feedback, and hand off only unresolved cases to humans. Enterprises adopt agentic RAG to deliver traceable, up-to-date answers across knowledge bases, legal corpora, or product catalogs while slashing hallucinations and manual curation.