Agentic RAG: Building AI Agents with Dynamic Knowledge Retrieval

TL;DR

• Agentic RAG combines the power of autonomous AI agents with RAG knowledge retrieval
• Agents can dynamically decide when and what to retrieve, unlike traditional RAG
• Modular architecture: planner, retriever, reasoner, executor
• Key patterns: ReAct, Plan-and-Execute, Self-RAG, Corrective RAG
• Use cases: research assistants, complex automation, multi-document analysis
• Try it now: Build your RAG agent on Ailog

Introduction: Beyond Traditional RAG

Traditional RAG (Retrieval-Augmented Generation) follows a linear pipeline: query → retrieval → generation. This approach works well for simple questions but reaches its limits when facing complex tasks that require:

• Multiple reasoning steps
• Combining information from multiple sources
• Dynamic decisions about what to search
• Validation and correction of retrieved information

Agentic RAG addresses these challenges by giving the AI the ability to plan, execute, and iterate autonomously. The agent becomes an intelligent orchestrator that decides when to retrieve, what to search for, and how to combine information to accomplish complex tasks.

What is Agentic RAG?

Definition

Agentic RAG is an architecture where an autonomous AI agent uses knowledge retrieval as one of its tools, among others, to accomplish tasks. Unlike traditional RAG where retrieval is systematic, the agent dynamically decides:

1. If retrieval is necessary
2. What to search for (optimal query formulation)
3. Where to search (source selection)
4. When to stop (sufficiency criteria)
5. How to combine results (multi-source synthesis)

Traditional RAG vs Agentic RAG

Why Agentic RAG?

Limitations of traditional RAG:

1. Complex questions: "Compare the pricing strategies of our 3 main competitors and recommend a position" requires multiple searches and synthesis.

2. Incomplete information: If the first retrieval isn't enough, traditional RAG can't search further.

3. Ambiguous queries: The agent can clarify or reformulate before searching.

4. Undetected hallucinations: The agent can verify its own responses against sources.

5. Multi-step tasks: Booking a trip requires searching flights, hotels, then combining and validating.

Agentic RAG Architecture

Overview

┌─────────────────────────────────────────────────────────────────┐
│                         AGENT CONTROLLER                         │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────────────┐  │
│  │   Planner    │  │   Reasoner   │  │    Memory Manager    │  │
│  └──────┬───────┘  └──────┬───────┘  └──────────┬───────────┘  │
│         │                 │                      │              │
│         └────────────┬────┴──────────────────────┘              │
│                      │                                           │
│              ┌───────▼───────┐                                   │
│              │   Executor    │                                   │
│              └───────┬───────┘                                   │
└──────────────────────┼──────────────────────────────────────────┘
                       │
        ┌──────────────┼──────────────┐
        │              │              │
   ┌────▼────┐   ┌────▼────┐   ┌────▼────┐
   │  Tool   │   │  Tool   │   │  Tool   │
   │Retrieval│   │ Compute │   │   API   │
   └─────────┘   └─────────┘   └─────────┘

Key Components

1. Planner

The planner decomposes complex tasks into manageable subtasks. It maintains an execution plan that can be dynamically revised.

DEVELOPERpython
class Planner:
    def __init__(self, llm):
        self.llm = llm

    def create_plan(self, task: str, context: dict) -> List[Step]:
        """Decompose a task into executable steps."""
        prompt = f"""
        Task: {task}
        Available context: {context}

        Decompose this task into clear, ordered steps.
        For each step, indicate:
        - The action to perform
        - Required tools
        - Dependencies on other steps
        """

        plan = self.llm.generate(prompt)
        return self.parse_plan(plan)

    def revise_plan(self, plan: List[Step], feedback: str) -> List[Step]:
        """Revise the plan based on intermediate results."""
        # Adapt plan if information is missing or changes
        pass

2. Reasoner

The reasoner analyzes retrieved information, identifies gaps, and decides on next actions.

DEVELOPERpython
class Reasoner:
    def __init__(self, llm):
        self.llm = llm

    def analyze_retrieval(self, query: str, documents: List[Document]) -> Analysis:
        """Analyze if retrieved documents are sufficient."""
        prompt = f"""
        Question: {query}
        Retrieved documents: {documents}

        Analysis:
        1. Do the documents answer the question?
        2. Is there missing information?
        3. Are there contradictions?
        4. What confidence do you have in the information?

        Decision: [SUFFICIENT | NEED_MORE | REFORMULATE | ESCALATE]
        """
        return self.llm.generate(prompt)

    def synthesize(self, query: str, all_results: List[RetrievalResult]) -> str:
        """Synthesize information from multiple retrievals."""
        pass

3. Memory Manager

Maintains conversational context and intermediate results.

DEVELOPERpython
class MemoryManager:
    def __init__(self):
        self.short_term = []  # Current conversation
        self.working_memory = {}  # Intermediate results
        self.episodic = []  # Action history

    def add_to_working_memory(self, key: str, value: any):
        """Store an intermediate result."""
        self.working_memory[key] = {
            "value": value,
            "timestamp": datetime.now(),
            "source": "retrieval"  # or "computation", "user"
        }

    def get_relevant_context(self, query: str) -> dict:
        """Retrieve relevant context for a query."""
        # Combine short-term memory and intermediate results
        pass

4. Executor

Orchestrates tool execution according to the plan.

DEVELOPERpython
class Executor:
    def __init__(self, tools: Dict[str, Tool]):
        self.tools = tools

    async def execute_step(self, step: Step) -> StepResult:
        """Execute a plan step."""
        tool = self.tools[step.tool_name]
        result = await tool.execute(step.parameters)

        return StepResult(
            step=step,
            result=result,
            success=result.is_valid(),
            metadata={"latency": result.latency}
        )

    async def execute_plan(self, plan: List[Step]) -> ExecutionResult:
        """Execute a complete plan with error handling."""
        results = []
        for step in plan:
            result = await self.execute_step(step)
            results.append(result)

            if not result.success and step.is_critical:
                # Trigger plan revision
                break

        return ExecutionResult(results=results)

Agentic RAG Patterns

1. ReAct Pattern (Reasoning + Acting)

ReAct alternates between reflection and action. The agent thinks aloud before each action.

Thought: I need to find the Q3 2024 revenue for company X.
Action: search_documents("Q3 2024 revenue company X")
Observation: Document found: Q3 2024 Financial Report, revenue = €45M
Thought: I have the revenue, now I need to compare it to Q3 2023.
Action: search_documents("Q3 2023 revenue company X")
Observation: Document found: Q3 2023 Financial Report, revenue = €38M
Thought: I can now calculate the growth.
Action: calculate((45-38)/38 * 100)
Observation: Result: 18.4%
Thought: I have all the information to answer.
Final Answer: Company X achieved €45M in revenue in Q3 2024, representing 18.4% growth compared to Q3 2023 (€38M).

Implementation:

DEVELOPERpython
class ReActAgent:
    def __init__(self, llm, tools):
        self.llm = llm
        self.tools = tools
        self.max_iterations = 10

    def run(self, query: str) -> str:
        history = []

        for i in range(self.max_iterations):
            # Generate next thought and action
            prompt = self.build_prompt(query, history)
            response = self.llm.generate(prompt)

            thought, action = self.parse_response(response)
            history.append({"thought": thought, "action": action})

            # Check if it's a final answer
            if action.type == "final_answer":
                return action.content

            # Execute the action
            observation = self.execute_action(action)
            history.append({"observation": observation})

        return "Unable to find a satisfactory answer."

2. Plan-and-Execute Pattern

Separates planning from execution for complex tasks.

DEVELOPERpython
class PlanAndExecuteAgent:
    def __init__(self, planner, executor, replanner):
        self.planner = planner
        self.executor = executor
        self.replanner = replanner

    async def run(self, task: str) -> str:
        # Phase 1: Initial planning
        plan = self.planner.create_plan(task)

        results = []
        for step in plan:
            # Phase 2: Execution
            result = await self.executor.execute_step(step)
            results.append(result)

            # Phase 3: Replanning if necessary
            if result.requires_replan:
                remaining_steps = plan[plan.index(step)+1:]
                plan = self.replanner.revise(
                    original_task=task,
                    completed=results,
                    remaining=remaining_steps,
                    feedback=result.feedback
                )

        return self.synthesize_results(results)

3. Self-RAG Pattern

The agent evaluates and critiques its own retrievals and generations.

DEVELOPERpython
class SelfRAGAgent:
    def __init__(self, llm, retriever):
        self.llm = llm
        self.retriever = retriever

    def run(self, query: str) -> str:
        # Step 1: Decide if retrieval is necessary
        need_retrieval = self.assess_retrieval_need(query)

        if need_retrieval:
            # Step 2: Retrieve
            documents = self.retriever.search(query)

            # Step 3: Critique relevance
            relevant_docs = self.critique_relevance(query, documents)

            if not relevant_docs:
                # Reformulate and retry
                new_query = self.reformulate_query(query)
                documents = self.retriever.search(new_query)
                relevant_docs = self.critique_relevance(query, documents)

        # Step 4: Generate response
        response = self.generate_response(query, relevant_docs)

        # Step 5: Critique response
        is_supported = self.critique_support(response, relevant_docs)
        is_useful = self.critique_usefulness(response, query)

        if not is_supported or not is_useful:
            # Regenerate with feedback
            response = self.regenerate_with_feedback(
                query, relevant_docs,
                support_feedback=is_supported,
                usefulness_feedback=is_useful
            )

        return response

4. Corrective RAG Pattern (CRAG)

Evaluates the quality of retrieved documents and takes corrective actions.

DEVELOPERpython
class CorrectiveRAGAgent:
    def __init__(self, llm, retriever, web_search):
        self.llm = llm
        self.retriever = retriever
        self.web_search = web_search

    def run(self, query: str) -> str:
        # Initial retrieval
        documents = self.retriever.search(query)

        # Quality evaluation
        relevance_scores = self.evaluate_relevance(query, documents)

        # Document classification
        correct_docs = [d for d, s in zip(documents, relevance_scores) if s > 0.7]
        ambiguous_docs = [d for d, s in zip(documents, relevance_scores) if 0.3 < s <= 0.7]
        incorrect_docs = [d for d, s in zip(documents, relevance_scores) if s <= 0.3]

        # Corrective actions based on case
        if len(correct_docs) >= 2:
            # Case: Sufficient documents
            final_docs = correct_docs
        elif len(correct_docs) + len(ambiguous_docs) >= 2:
            # Case: Need to refine ambiguous ones
            refined = self.refine_ambiguous(query, ambiguous_docs)
            final_docs = correct_docs + refined
        else:
            # Case: Need external search
            web_results = self.web_search.search(query)
            final_docs = correct_docs + self.process_web_results(web_results)

        # Generation with corrected documents
        return self.generate_response(query, final_docs)

Practical Implementation

Setting Up a RAG Agent with LangChain

DEVELOPERpython
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain.tools import Tool
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate

# Define tools
def search_knowledge_base(query: str) -> str:
    """Search in the internal knowledge base."""
    # Vector search implementation
    results = vector_store.similarity_search(query, k=5)
    return "\n".join([doc.page_content for doc in results])

def search_web(query: str) -> str:
    """Search the web for recent information."""
    # Web search implementation
    pass

def calculate(expression: str) -> str:
    """Perform a mathematical calculation."""
    return str(eval(expression))

tools = [
    Tool(
        name="knowledge_search",
        func=search_knowledge_base,
        description="Search internal documentation and knowledge bases. Use for company-specific information."
    ),
    Tool(
        name="web_search",
        func=search_web,
        description="Search the web. Use for recent or public information."
    ),
    Tool(
        name="calculator",
        func=calculate,
        description="Perform mathematical calculations. Input: valid mathematical expression."
    )
]

# Create prompt
prompt = ChatPromptTemplate.from_messages([
    ("system", """You are an expert research assistant. You use your tools judiciously to answer questions.

Rules:
1. Always start by thinking about what you need
2. Use knowledge_search for internal information
3. Use web_search for recent or external information
4. Verify your information before concluding
5. Cite your sources in your final answer"""),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}")
])

# Create agent
llm = ChatOpenAI(model="gpt-4-turbo-preview", temperature=0)
agent = create_openai_tools_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Execute
response = agent_executor.invoke({
    "input": "Compare our Q3 2024 sales with the market average"
})

Multi-Source Management with Routing

DEVELOPERpython
class MultiSourceRouter:
    """Routes queries to appropriate sources."""

    def __init__(self, sources: Dict[str, VectorStore], llm):
        self.sources = sources
        self.llm = llm

    def route(self, query: str) -> List[str]:
        """Determine which sources to query."""
        prompt = f"""
        Query: {query}

        Available sources:
        - technical_docs: Technical docs, APIs, architecture
        - customer_base: Customer information, contracts, history
        - finance: Financial reports, budgets, forecasts
        - hr: HR policies, org chart, procedures
        - products: Product catalog, pricing, specs

        Which sources are relevant for this query?
        Respond with a JSON list: ["source1", "source2"]
        """

        response = self.llm.generate(prompt)
        return json.loads(response)

    async def search_all(self, query: str) -> Dict[str, List[Document]]:
        """Parallel search across all relevant sources."""
        relevant_sources = self.route(query)

        tasks = [
            self.search_source(source, query)
            for source in relevant_sources
        ]

        results = await asyncio.gather(*tasks)
        return dict(zip(relevant_sources, results))

Validation and Self-Correction

DEVELOPERpython
class ResponseValidator:
    """Validates and corrects generated responses."""

    def __init__(self, llm):
        self.llm = llm

    def validate(self, query: str, response: str, sources: List[Document]) -> ValidationResult:
        prompt = f"""
        Question: {query}
        Generated response: {response}
        Sources used: {[doc.page_content for doc in sources]}

        Evaluate this response:
        1. FACTUALITY: Is each claim supported by sources? (yes/no/partial)
        2. COMPLETENESS: Does the response cover all aspects of the question? (yes/no)
        3. COHERENCE: Is the response logically coherent? (yes/no)
        4. HALLUCINATIONS: Is there information not present in sources? (list)

        JSON format:
        {{
            "factuality": "yes|no|partial",
            "completeness": "yes|no",
            "coherence": "yes|no",
            "hallucinations": ["...", "..."],
            "confidence": 0.0-1.0,
            "corrections_needed": ["...", "..."]
        }}
        """

        result = self.llm.generate(prompt)
        return ValidationResult.from_json(result)

    def correct(self, query: str, response: str, validation: ValidationResult, sources: List[Document]) -> str:
        """Correct response based on validation."""
        if validation.confidence > 0.9:
            return response

        prompt = f"""
        Original response: {response}
        Identified issues: {validation.corrections_needed}
        Hallucinations: {validation.hallucinations}
        Correct sources: {[doc.page_content for doc in sources]}

        Generate a corrected response that:
        1. Eliminates hallucinations
        2. Relies only on sources
        3. Remains complete and useful
        """

        return self.llm.generate(prompt)

Advanced Use Cases

1. Multi-Document Research Assistant

Analyzes and synthesizes information from numerous documents.

DEVELOPERpython
class ResearchAssistant:
    """Research assistant capable of analyzing multiple documents."""

    async def research(self, topic: str, depth: str = "comprehensive") -> ResearchReport:
        # Phase 1: Initial exploration
        initial_results = await self.broad_search(topic)

        # Phase 2: Subtopic identification
        subtopics = self.identify_subtopics(topic, initial_results)

        # Phase 3: Deep search per subtopic
        detailed_results = {}
        for subtopic in subtopics:
            results = await self.deep_search(subtopic)
            detailed_results[subtopic] = results

        # Phase 4: Contradiction identification
        contradictions = self.find_contradictions(detailed_results)

        # Phase 5: Synthesis
        report = self.synthesize_report(
            topic=topic,
            subtopics=subtopics,
            results=detailed_results,
            contradictions=contradictions
        )

        return report

2. Due Diligence Agent

Automates in-depth analysis for business decisions.

DEVELOPERpython
class DueDiligenceAgent:
    """Agent for automated due diligence analysis."""

    def analyze_company(self, company_name: str) -> DueDiligenceReport:
        sections = [
            ("financial", self.analyze_financials),
            ("legal", self.analyze_legal),
            ("market", self.analyze_market_position),
            ("team", self.analyze_leadership),
            ("tech", self.analyze_technology),
            ("risks", self.identify_risks)
        ]

        results = {}
        for section_name, analyzer in sections:
            results[section_name] = analyzer(company_name)

        # Synthesis and scoring
        return self.compile_report(company_name, results)

3. Intelligent Customer Support Agent

Solves complex problems by consulting multiple sources.

DEVELOPERpython
class SupportAgent:
    """Customer support agent with multi-step resolution."""

    async def handle_ticket(self, ticket: SupportTicket) -> Resolution:
        # Understand the problem
        problem_analysis = self.analyze_problem(ticket)

        # Search for solutions
        kb_results = await self.search_knowledge_base(problem_analysis.keywords)
        past_tickets = await self.search_similar_tickets(problem_analysis)

        # Evaluate potential solutions
        solutions = self.evaluate_solutions(kb_results, past_tickets)

        if solutions.best.confidence > 0.8:
            return self.generate_resolution(solutions.best)
        else:
            # Escalate with enriched context
            return self.escalate_with_context(ticket, problem_analysis, solutions)

Optimization and Best Practices

1. Token and Cost Management

DEVELOPERpython
class TokenOptimizer:
    """Optimizes token usage in agents."""

    def __init__(self, max_tokens_per_step: int = 2000):
        self.max_tokens = max_tokens_per_step

    def compress_context(self, documents: List[Document], query: str) -> str:
        """Compress context to respect limits."""
        # Sort by relevance
        scored = [(doc, self.relevance_score(doc, query)) for doc in documents]
        scored.sort(key=lambda x: x[1], reverse=True)

        # Select up to limit
        selected = []
        token_count = 0
        for doc, score in scored:
            doc_tokens = self.count_tokens(doc.page_content)
            if token_count + doc_tokens <= self.max_tokens:
                selected.append(doc.page_content)
                token_count += doc_tokens

        return "\n---\n".join(selected)

2. Search Parallelization

DEVELOPERpython
async def parallel_search(queries: List[str], retrievers: List[Retriever]) -> Dict:
    """Execute multiple searches in parallel."""
    tasks = []
    for query in queries:
        for retriever in retrievers:
            tasks.append(retriever.search(query))

    results = await asyncio.gather(*tasks, return_exceptions=True)

    # Group and deduplicate results
    return deduplicate_results(results)

3. Intelligent Caching

DEVELOPERpython
class AgentCache:
    """Intelligent cache for agent results."""

    def __init__(self, ttl: int = 3600):
        self.cache = {}
        self.ttl = ttl

    def get_or_compute(self, key: str, compute_fn: Callable) -> Any:
        # Check cache
        if key in self.cache:
            entry = self.cache[key]
            if time.time() - entry["timestamp"] < self.ttl:
                return entry["value"]

        # Compute and cache
        result = compute_fn()
        self.cache[key] = {
            "value": result,
            "timestamp": time.time()
        }
        return result

4. Error Handling and Fallbacks

DEVELOPERpython
class ResilientAgent:
    """Agent with robust error handling."""

    async def execute_with_fallback(self, action: Action) -> Result:
        strategies = [
            (action.primary_tool, action.params),
            (action.fallback_tool, action.params),
            (self.web_search, {"query": action.query}),
            (self.ask_user, {"question": f"I couldn't find: {action.query}"})
        ]

        for tool, params in strategies:
            try:
                result = await asyncio.wait_for(
                    tool.execute(params),
                    timeout=30.0
                )
                if result.is_valid():
                    return result
            except Exception as e:
                self.log_error(e, tool, params)
                continue

        return Result.failure("All strategies failed")

Evaluating RAG Agents

Key Metrics

1. Resolution rate: Percentage of queries resolved without human intervention
2. Step count: Reasoning efficiency (fewer = better)
3. Retrieval precision: Relevance of found documents
4. Faithfulness: Responses based on sources vs hallucinations
5. End-to-end latency: Total resolution time

Evaluation Framework

DEVELOPERpython
class AgentEvaluator:
    """Evaluates RAG agent performance."""

    def evaluate(self, agent: Agent, test_cases: List[TestCase]) -> EvaluationReport:
        metrics = {
            "resolution_rate": [],
            "steps_count": [],
            "retrieval_precision": [],
            "faithfulness": [],
            "latency": []
        }

        for case in test_cases:
            start = time.time()
            result = agent.run(case.query)
            latency = time.time() - start

            metrics["latency"].append(latency)
            metrics["resolution_rate"].append(
                self.check_resolution(result, case.expected)
            )
            metrics["faithfulness"].append(
                self.check_faithfulness(result, agent.last_sources)
            )
            # ... other metrics

        return EvaluationReport(
            avg_resolution_rate=np.mean(metrics["resolution_rate"]),
            avg_latency=np.mean(metrics["latency"]),
            # ...
        )

Conclusion

Agentic RAG represents the natural evolution of RAG systems toward greater autonomy and intelligence. By combining planning, reasoning, and dynamic retrieval, these agents can solve complex tasks that exceed the capabilities of traditional RAG.

Key takeaways:

1. Think agent, not pipeline: The agent dynamically decides its actions
2. Modularity: Separate planning, execution, and evaluation
3. Continuous validation: The agent must critique its own results
4. Optimization: Parallelize, cache, and manage tokens
5. Resilience: Plan for fallbacks and robust error handling

Agentic RAG paves the way for AI assistants truly capable of autonomous research, complex analysis, and multi-step reasoning. It's the foundation for future AI systems capable of working autonomously on sophisticated tasks.

Additional Resources

• Ailog Documentation - Comprehensive RAG guides
• LangChain Agents - Agent framework
• ReAct Paper - Original ReAct pattern
• Self-RAG Paper - Self-Reflective RAG
• CRAG Paper - Corrective RAG