Agentic RAG

Why It Matters

Traditional RAG follows a rigid pattern: retrieve documents, stuff them into a prompt, generate a response. This works for simple factual queries but fails when questions require multi-step reasoning, cross-referencing multiple sources, or determining that retrieval isn’t even needed.

Agentic RAG solves this by giving the AI agent control over the retrieval process itself. Instead of blindly retrieving on every query, the agent decides: Do I need to retrieve? From which sources? Should I retrieve again after seeing initial results? Should I reformulate my query?

For AI engineers, agentic RAG represents the maturation of RAG from a technique to an architecture. As you build more sophisticated applications, you’ll find that static retrieval pipelines hit walls. Agentic approaches let your system adapt to query complexity, making smarter use of context windows and providing more accurate responses for complex questions.

How It Works

Agentic RAG differs from naive RAG in several key ways:

1. Retrieval as a Tool Instead of retrieval being a mandatory first step, it becomes a tool the agent can invoke. Simple questions like “What’s your refund policy?” might not need retrieval if the answer is in the system prompt. Complex questions might need multiple retrieval calls across different data sources.

2. Query Planning Before retrieving, the agent analyzes the query. Is this a simple factual lookup or a multi-part question? Does it require information from multiple domains? The agent breaks down complex queries into sub-questions, each potentially requiring different retrieval strategies.

3. Adaptive Retrieval After initial retrieval, the agent evaluates: Did I get enough information? Are there gaps? Should I search with different keywords? This iterative approach means the agent keeps retrieving until it has sufficient context, or determines the information isn’t available.

4. Source Selection With multiple knowledge bases or retrieval methods available, the agent routes queries to the appropriate source. Technical questions go to documentation; customer-specific questions hit the CRM; general questions might not need retrieval at all.

Implementation Basics

Building agentic RAG requires treating retrieval as a callable tool within your agent framework:

Define Retrieval Tools Create distinct tools for different retrieval actions: search documentation, query customer data, fetch recent context. Each tool should have clear descriptions so the agent knows when to use it.

Add Reasoning Prompts Your system prompt should instruct the agent to reason about retrieval needs. Include guidance like: “Before answering, consider whether you need additional context. For factual questions about our products, use the documentation search tool.”

Implement Iterative Loops Allow the agent to retrieve, evaluate, and retrieve again. Set reasonable limits (typically 2-3 retrieval iterations maximum) to prevent infinite loops while enabling multi-step reasoning.

Track What’s Retrieved Maintain state about what documents have been retrieved to avoid redundant searches. This also helps with attribution and debugging.

Start with a simple agentic pattern: let the agent decide whether to retrieve at all. This single decision point often improves performance significantly before you add more sophisticated multi-step retrieval logic.