Agentic RAG: When Your Knowledge Base Starts Thinking for Itself

Standard RAG (Retrieval Augmented Generation) was a breakthrough: instead of relying solely on training data, AI systems could pull in fresh, relevant documents before answering questions. Suddenly, AI assistants could know your company's policies, products, and processes.

But standard RAG has a ceiling. It retrieves, but it doesn't think about what it's retrieving.

Agentic RAG changes that.

The Limitations of Traditional RAG

If you've built a RAG system, you've probably hit these walls:

Single-source tunnel vision. Traditional RAG queries one vector store and returns whatever's semantically closest. It can't decide "this question needs data from the CRM and the knowledge base and last month's board report."

Naive retrieval. The system embeds your query and finds similar chunks. But if your question is ambiguous or multi-part, it retrieves a muddled set of partially-relevant documents.

No self-correction. If the retrieved documents don't contain the answer, traditional RAG either hallucinates or gives a generic "I don't have that information." It can't think "maybe I should search differently."

Static pipeline. Query → retrieve → generate. Every question follows the same path regardless of complexity. A simple factual lookup gets the same treatment as a nuanced analytical question.

What Makes RAG "Agentic"?

Agentic RAG wraps the retrieval process in an AI agent that can plan, decide, and iterate. Instead of a fixed pipeline, you get an intelligent orchestrator that:

1. Plans Its Retrieval Strategy

Before searching, the agent analyses the query and decides:

• Which knowledge sources to query (and in what order)
• Whether to decompose a complex question into sub-questions
• What retrieval method suits each sub-question (semantic search, keyword search, SQL query, API call)

A question like "How did our Q4 revenue compare to forecast, and what drove the variance?" gets decomposed into: (1) retrieve Q4 actuals, (2) retrieve Q4 forecast, (3) retrieve variance analysis or commentary.

2. Routes to the Right Source

Enterprise knowledge lives everywhere: SharePoint, Confluence, Slack, databases, email archives, PDF repositories, CRM notes. Agentic RAG maintains awareness of multiple sources and routes queries intelligently.

The agent knows that financial data lives in the ERP, product specs live in Confluence, and customer feedback lives in the CRM. It doesn't dump everything into one vector store and hope for the best.

3. Evaluates and Refines

After initial retrieval, the agent assesses: "Do these documents actually answer the question?" If not, it:

• Reformulates the query with different terms
• Searches additional sources
• Asks clarifying sub-questions
• Combines partial answers from multiple retrievals

This self-correcting loop is what separates agentic from traditional RAG. The system knows when it doesn't have a good answer and does something about it.

4. Synthesises Across Sources

Rather than dumping retrieved chunks into a prompt, the agent reasons across sources. It can reconcile conflicting information ("the product sheet says X but the latest engineering update says Y—the update is newer and takes precedence"), identify gaps, and present a coherent synthesised answer.

Architecture Patterns

The Router Pattern

The simplest agentic RAG: an LLM examines the query and routes it to the appropriate retrieval pipeline.

User Query → Router Agent → [Knowledge Base A | Database B | API C] → Generate

Good for: Organisations with clearly distinct knowledge domains. Low complexity, high impact.

The Planner-Executor Pattern

A planning agent decomposes queries, assigns sub-tasks to specialised retrieval agents, then synthesises results.

User Query → Planner → [Sub-query 1 → Agent A]
                       [Sub-query 2 → Agent B]  → Synthesiser → Response
                       [Sub-query 3 → Agent C]

Good for: Complex analytical questions spanning multiple domains. Higher latency, much richer answers.

The Iterative Refinement Pattern

A single agent retrieves, evaluates, and re-retrieves in a loop until it's confident in the answer quality.

User Query → Retrieve → Evaluate → [Good enough? → Generate]
                                   [Not enough? → Reformulate → Retrieve again]

Good for: Precision-critical applications where wrong answers are costly. Legal, medical, compliance.

The Multi-Agent Debate Pattern

Multiple agents retrieve independently and then "debate" their findings, surfacing disagreements and resolving them through evidence.

User Query → [Agent 1 retrieves + reasons]  → Debate/Reconcile → Response
            [Agent 2 retrieves + reasons]
            [Agent 3 retrieves + reasons]

Good for: High-stakes decisions where you want multiple perspectives and robust fact-checking.

Real-World Use Cases

Internal Knowledge Assistant

A manufacturing company has procedures in SharePoint, quality records in a database, supplier specs in PDFs, and tribal knowledge in Slack. Traditional RAG struggles because the answer often spans three sources.

Agentic RAG: "What's the approved torque specification for the Model X assembly, and when was it last updated?" The agent queries the engineering database for the spec, cross-references with the latest quality notice, and checks Slack for any recent discussions about changes.

Customer Support Escalation

When a support agent asks "Has this customer reported this issue before, and what was the resolution?", the system needs to search the ticketing system, check the CRM for account notes, and review the knowledge base for known issues.

Agentic RAG routes each sub-query to the right system and synthesises: "Yes, they reported a similar issue in October. It was resolved by updating firmware to v3.2. However, a new bulletin from engineering suggests v3.4 is now required for their hardware revision."

Compliance and Audit

"Does our current data processing agreement with Vendor X comply with the latest GDPR amendments?" The agent retrieves the DPA, the current GDPR text, recent regulatory guidance, and any internal compliance memos—then reasons about whether the DPA's clauses satisfy each requirement.

Implementation Considerations

Start Simple, Add Agency Gradually

Don't build the multi-agent debate pattern on day one. Start with:

1. A solid traditional RAG system (good chunking, good embeddings, good prompts)
2. Add query routing to multiple sources
3. Add self-evaluation ("is this answer grounded in the retrieved documents?")
4. Add iterative refinement for low-confidence answers
5. Consider multi-agent patterns only for complex, high-value use cases

Latency vs. Quality Trade-off

Every agentic step adds latency. A traditional RAG response takes 1-3 seconds. A planner-executor pattern might take 5-15 seconds. Users accept this for complex questions but not for simple ones.

Solution: Use a complexity classifier. Simple factual queries get fast traditional RAG. Complex analytical questions get the full agentic pipeline.

Cost Management

More LLM calls means higher costs. An agentic RAG system might make 3-10x the LLM calls of traditional RAG for a single query. Mitigate with:

• Smaller, faster models for routing and evaluation
• Caching frequent queries and their retrieval plans
• Setting iteration limits (max 3 refinement loops)
• Using traditional RAG as the default, escalating to agentic only when needed

Observability Is Essential

With multiple retrieval steps and decision points, you need to trace what the agent did and why. Log:

• The agent's retrieval plan
• Which sources were queried
• What was retrieved (and what was discarded)
• Any refinement steps and why they were triggered
• The final synthesis reasoning

Without this, debugging "why did it give a wrong answer?" becomes impossible.

The Tooling Landscape

Several frameworks now support agentic RAG natively:

• LlamaIndex has built-in agentic RAG with query planning and sub-question decomposition
• LangGraph provides the state machine primitives to build custom agentic retrieval flows
• CrewAI can model research teams where different agents specialise in different knowledge domains
• Haystack (by deepset) supports pipeline branching and agent-driven retrieval
• Microsoft Semantic Kernel integrates with enterprise data sources and supports agentic patterns

The choice depends on your existing stack and complexity needs. LlamaIndex is often the fastest path for teams already using it for RAG.

When You Don't Need Agentic RAG

Not every use case benefits from agency. Standard RAG is perfectly fine when:

• Questions are simple and factual
• Knowledge lives in a single, well-structured source
• Latency requirements are tight (<2 seconds)
• The cost of occasional imperfect retrieval is low
• Your team doesn't have the engineering capacity to maintain complex pipelines

Agentic RAG shines when the questions are complex, the knowledge is scattered, and accuracy matters more than speed.

The Trajectory

We're seeing agentic RAG become the default architecture for serious enterprise knowledge systems. The pattern addresses the most common complaints about basic RAG ("it doesn't find the right stuff," "it can't combine information from different sources") in a principled way.

Within the next 12-18 months, expect:

• Framework-level support to make agentic RAG nearly as easy to set up as traditional RAG
• Better evaluation tools for measuring retrieval quality at each stage
• Hybrid approaches that dynamically choose between simple and agentic retrieval
• Tighter integration with enterprise data platforms (Snowflake, Databricks, Microsoft Fabric)

The companies building agentic RAG today are the ones whose AI assistants will actually be useful—not just impressive demos, but tools people rely on daily.

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Need help evolving your RAG system from retrieval to reasoning? Caversham Digital designs and builds agentic knowledge systems for UK businesses. Let's talk.