¿Qué es RAG agéntico?
En términos técnicos, A retrieval architecture in which an AI agent decides when, where, and how to retrieve knowledge during a multi-step reasoning process instead of always running one fixed retrieval step before generation.
¿Por qué es importante?
Es importante porque Agentic RAG can combine multiple knowledge systems, reformulate queries, inspect intermediate evidence, and retrieve again when information is missing. This flexibility is valuable for complex research but adds latency, cost, and control risk.
Cómo funciona
El sistema funciona así: The agent evaluates the task, selects a retrieval tool, generates a query, inspects results, updates its plan, optionally searches another source or graph, and stops when evidence satisfies its completion criteria.
Ejemplo técnico
Ejemplo: An account-research agent searches CRM records, traverses company relationships, retrieves product documentation, checks recent web sources, and cites the evidence used in its final brief.
Notas de implementación
Notas técnicas: Bound retrieval steps, constrain allowed sources, validate generated queries, enforce permissions in every tool, detect repeated searches, record provenance, and evaluate whether additional retrieval materially improves the answer.
