What is agentic RAG?
Agentic RAG combines retrieval-augmented generation with AI agent capabilities. Instead of a simple query → retrieve → generate pipeline, an agentic RAG system can decide what to search for, query multiple data sources, evaluate the results, and iterate until it has enough information to give a complete answer.
Think of it this way: standard RAG is like asking a librarian to find you a book. Agentic RAG is like asking a research assistant to investigate a question. They'll check multiple sources, cross-reference findings, and come back with a synthesised answer.
Standard RAG vs agentic RAG
| Capability | Standard RAG | Agentic RAG |
|---|---|---|
| Query approach | Single retrieval pass | Multiple queries, iterative |
| Data sources | One vector database | Multiple (vector DBs, APIs, databases) |
| Reasoning | Generate from retrieved context | Reason → retrieve → evaluate → repeat |
| Actions | Answer generation only | Can take actions (update, notify, route) |
| Complexity | Moderate | Higher |
| Control | Predictable | Requires guardrails |
How agentic RAG works
An agentic RAG system typically follows this pattern:
- Plan: The agent analyses the user's question and determines what information it needs and where to find it.
- Retrieve: It queries one or more knowledge sources: vector databases, structured databases, APIs, or even web search.
- Evaluate: It checks whether the retrieved information is sufficient to answer the question.
- Refine: If not, it reformulates the query, searches different sources, or breaks the question into sub-questions.
- Synthesise: It combines information from multiple retrievals into a coherent answer.
- Act (optional): It takes action based on its findings, such as sending a notification, updating a record, or routing a task.
The key innovation: The agent decides how to search, not just what to search. It can decompose complex questions, query different systems, and iteratively refine its understanding.
Business use cases
- Cross-system knowledge queries: "What's the status of Project Alpha, including budget from finance, timeline from PM, and latest client feedback from CRM?"
- Compliance investigations: "Does our handling of customer data in the new feature comply with both the Privacy Act and our internal data governance policy?"
- Multi-step research: "Find all incidents related to Equipment Type X, summarise the root causes, and check if the recommended maintenance schedule has been updated."
- Intelligent triage: Reading an incoming request, looking up relevant policies, checking the customer's history, and routing to the right team with context.
Trade-offs and risks
Agentic RAG is more powerful but comes with added complexity:
- Latency: Multiple retrieval steps mean slower response times. Seconds, not milliseconds.
- Cost: More LLM calls per query. Each reasoning step and retrieval costs API tokens.
- Controllability: The agent makes decisions about what to search and how to interpret results. This requires careful prompt engineering and guardrails.
- Debugging: When something goes wrong, the reasoning chain can be hard to trace.
- Hallucination risk: More reasoning steps means more opportunities for the model to introduce errors.
Our advice: Start with standard RAG. Only move to agentic RAG when you have a proven need for multi-source reasoning or action-taking. Don't add complexity for its own sake.
When to consider it
Agentic RAG makes sense when:
- Questions routinely span multiple knowledge sources or systems
- Users ask complex, multi-part questions that require synthesis
- The system needs to take actions based on what it finds, not just answer
- You've already built a standard RAG system and users are hitting its limits
For most initial deployments, standard RAG covers 80% of use cases. Agentic RAG is for the remaining 20% where the complexity is justified.
Key takeaways
- Agentic RAG combines the accuracy of RAG with the autonomy of AI agents.
- It can query multiple knowledge sources, reason across them, and take actions based on what it finds.
- More powerful than standard RAG, but also more complex and harder to control.
- Best for scenarios where questions span multiple systems or require multi-step reasoning.
