Agentic RAG combines the knowledge retrieval power of RAG with the autonomous decision-making capabilities of AI agents. The result? Systems that don't just find information — they act on it.
RAG vs Agentic RAG
Traditional RAG: User asks question → System retrieves relevant documents → LLM generates answer from retrieved context.
Agentic RAG: User describes a goal → Agent plans a multi-step approach → Agent retrieves, analyzes, and synthesizes across multiple sources → Agent takes actions or provides comprehensive recommendations.
Enterprise Use Cases
Legal Document Review
An agentic RAG system can review contracts, identify non-standard clauses, compare against your standard terms, and flag risks — all without human intervention for routine agreements.
Financial Research
Rather than answering a single question, an agentic system can conduct a full research workflow: gather market data, analyze competitor filings, pull internal performance metrics, and synthesize a comprehensive briefing.
IT Support Automation
When an employee reports an issue, the agentic system can diagnose the problem by querying multiple knowledge bases, attempt automated fixes, and only escalate to human support when necessary.
Sales Intelligence
Before a sales call, an agentic RAG system can research the prospect company, identify decision-makers, find relevant case studies, check CRM history, and prepare a personalized briefing — all automatically.
How to Get Started
Identify high-value workflows that involve multi-step information gathering
Audit your knowledge sources — databases, documents, APIs that agents will access
Start with a supervised agent that proposes actions for human approval
Gradually increase autonomy as confidence in the system grows
Iedeo specializes in building agentic RAG systems for enterprise. Learn more about our RAG solutions or contact us to discuss your use case.