Agentic RAG: Combining Retrieval and Autonomous Workflows

Clawpedia · For Humans

Learn how to combine retrieval-augmented generation with agentic workflows for powerful AI applications.

Agentic RAG: Combining Retrieval and Autonomous Workflows

Retrieval-Augmented Generation (RAG) has become a standard technique for grounding AI responses in real data. Agentic RAG takes this further by giving the AI agent autonomy to decide when, what, and how to retrieve — turning passive lookup into active research.

This guide explains how Agentic RAG works, how it differs from basic RAG, and how OpenClaw implements it.

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What Is RAG?

Traditional RAG follows a simple pipeline:

User Query → Search Knowledge Base → Inject Results into Prompt → Generate Answer

The AI does not choose whether to search — it always searches. It does not refine queries or combine multiple sources. It takes what it gets and generates an answer.

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What Is Agentic RAG?

Agentic RAG adds decision-making to the retrieval process:

User Query
    │
    ▼
┌─────────────────────┐
│ Agent: Do I need to │
│ retrieve anything?  │
└────────┬────────────┘
         │ Yes
         ▼
┌─────────────────────┐
│ Agent: What should  │
│ I search for?       │
│ (may reformulate)   │
└────────┬────────────┘
         │
         ▼
┌─────────────────────┐
│ Search Source A     │──► Not enough info?
│ Search Source B     │    │
│ Search Source C     │    ▼ Search again with
└────────┬────────────┘    refined query
         │
         ▼
┌─────────────────────┐
│ Agent: Synthesize   │
│ answer from all     │
│ retrieved context   │
└─────────────────────┘

Key differences:

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How OpenClaw Implements Agentic RAG

OpenClaw's retrieval system follows an agentic pattern by default when skills provide knowledge sources.

Setting Up Knowledge Sources

# Add a local document folder
openclaw knowledge add ./docs --name "project-docs"

# Add a website
openclaw knowledge add https://docs.example.com --name "api-docs" --crawl

# Add a database
openclaw knowledge add postgresql://... --name "customer-data"

How It Works in Practice

You: What's our refund policy for enterprise customers?

Agent thinking:
  1. This requires specific policy information → need retrieval
  2. Search "project-docs" for "refund policy enterprise"
  3. Found general refund policy but no enterprise-specific info
  4. Reformulate: search for "enterprise terms" and "SLA conditions"
  5. Found enterprise SLA document with refund clause
  6. Combine both sources for complete answer

OpenClaw: Based on our documentation, enterprise customers have a 
          30-day refund window (vs. 14 days for standard plans)...

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The Agentic RAG Loop

Step 1: Intent Classification

The agent first decides if retrieval is needed:

• No retrieval: General knowledge questions, casual chat, math
• Retrieval needed: Company-specific data, recent events, user documents

Step 2: Query Planning

The agent creates a retrieval strategy:

Original query: "How do our API rate limits compare to competitors?"

Planned searches:
  1. Internal docs: "API rate limits" → our limits
  2. Internal docs: "competitor analysis" → if available
  3. Web search: "[competitor] API rate limits 2025" → external data

Step 3: Retrieval Execution

Searches are executed, potentially in parallel:

# OpenClaw searches multiple sources
[knowledge] Searching "project-docs" for "API rate limits"... 3 results
[knowledge] Searching "api-docs" for "rate limiting"... 5 results
[web] Searching for "competitor API rate limits"... 4 results

Step 4: Evaluation & Re-Retrieval

The agent evaluates results:

• Sufficient: Proceed to synthesis
• Partial: Reformulate query and search again
• Irrelevant: Try different sources or acknowledge the gap

Step 5: Synthesis

The agent combines all retrieved context into a coherent answer, citing sources.

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Building an Agentic RAG Skill

// skills/research-assistant/index.js
export default {
  name: "research-assistant",
  description: "Multi-source research with iterative retrieval",
  
  execute: async (context) => {
    const { query, knowledge, web } = context;
    
    // Step 1: Plan retrieval
    const plan = await context.llm.chat(
      `Given this question: "${query}"
       What sources should I search and what queries should I use?
       Available sources: ${knowledge.listSources()}`
    );
    
    // Step 2: Execute searches
    const results = [];
    for (const search of plan.searches) {
      const docs = await knowledge.search(search.source, search.query);
      results.push(...docs);
    }
    
    // Step 3: Evaluate
    const evaluation = await context.llm.chat(
      `Are these results sufficient to answer: "${query}"?
       Results: ${JSON.stringify(results)}`
    );
    
    // Step 4: Re-retrieve if needed
    if (evaluation.needsMore) {
      const moreResults = await knowledge.search(
        evaluation.suggestedSource,
        evaluation.refinedQuery
      );
      results.push(...moreResults);
    }
    
    // Step 5: Synthesize
    return context.llm.chat(
      `Answer this question: "${query}"
       Using these sources: ${JSON.stringify(results)}
       Cite your sources.`
    );
  }
};

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Agentic RAG Patterns

Corrective RAG

The agent checks if retrieved documents are actually relevant before using them:

Query: "Python installation guide"
Retrieved: Document about Python snakes
Agent: Irrelevant → re-search with "Python programming language installation"

Self-RAG

The agent critiques its own generated answer and retrieves more if it detects gaps:

Generated: "The API supports 1000 requests per minute..."
Self-check: "Am I confident about this number?" → No → Re-retrieve

Adaptive RAG

The agent adjusts retrieval strategy based on query complexity:

• Simple query: Single search, one source
• Complex query: Multiple searches, multiple sources, iterative refinement
• Comparison query: Parallel searches across different sources

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When to Use Agentic RAG

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Best Practices

• Index your documents well: Good retrieval starts with good indexing
• Use metadata: Tag documents with dates, categories, and authors
• Set retrieval limits: Cap the number of re-retrieval loops (3–5 max)
• Monitor costs: Each retrieval loop uses additional LLM tokens
• Cache results: Store frequently retrieved answers
• Log retrieval chains: Debug by reviewing the agent's search decisions

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Summary

Agentic RAG transforms retrieval from a passive lookup into an active research process. By giving the agent control over when, what, and how to retrieve, you get more accurate, comprehensive answers — especially for complex questions that span multiple sources. OpenClaw's skill system makes it straightforward to build custom Agentic RAG workflows tailored to your specific knowledge base.