Agentic RAG:¶

What is RAG First?¶

RAG stands for Retrieval-Augmented Generation

Think of it like an open-book exam: - Traditional AI: Answers only from what it memorized (like a closed-book exam) - RAG: Can look up information in documents before answering (like an open-book exam)

Basic RAG Flow:¶

User asks a question: "What was our Q3 revenue?"
Retrieval: System searches documents and finds relevant information
Augmentation: Adds the retrieved info to the AI's context
Generation: AI generates an answer using both its knowledge AND the retrieved documents

What is Agentic RAG?¶

Agentic RAG = RAG + Agency (autonomous decision-making)

Instead of just one simple retrieve-and-answer step, the AI acts like an intelligent agent that: - Plans its approach - Decides which sources to check - Determines if it needs more information - Iteratively refines its search - Validates its findings

The Key Difference:¶

Basic RAG (Passive):

Question → Retrieve docs → Generate answer

Agentic RAG (Active):

Question → Plan approach → Retrieve → Evaluate → Need more? → Retrieve again → Synthesize → Verify → Answer

Real-World Example¶

Scenario: Student asks "Compare the economic policies of the last three presidents"¶

Basic RAG would: 1. Search for "economic policies presidents" 2. Retrieve top 5 documents 3. Generate answer from those documents 4. Done ✓

Agentic RAG would: 1. Plan: "I need info on 3 different presidents, so I should search for each separately" 2. Execute searches: - Search "Biden economic policy" - Search "Trump economic policy" - Search "Obama economic policy" 3. Evaluate: "Do I have enough detail on fiscal policy? What about trade?" 4. Refine searches: - Search "Biden fiscal policy tax" - Search "Trump trade tariffs" 5. Synthesize: Organize findings into coherent comparison 6. Verify: Check if any claims conflict, cross-reference sources 7. Generate: Produce comprehensive answer

Core Components of Agentic RAG¶

1. Planning & Reasoning¶

The agent breaks down complex questions into sub-tasks

Question: "How has remote work affected tech company productivity?"

Agent's Plan:
- Sub-task 1: Find data on remote work adoption in tech
- Sub-task 2: Find productivity metrics before/after
- Sub-task 3: Find expert opinions on causation
- Sub-task 4: Synthesize findings

2. Tool Use¶

The agent can use multiple tools, not just simple search

Available Tools: - Semantic search (find similar concepts) - Keyword search (find exact terms) - SQL queries (structured data) - Web search (current info) - Calculator (compute statistics) - Code execution (analyze data)

3. Iterative Retrieval¶

The agent can retrieve multiple times based on what it learns

Initial query → Find gap in knowledge → Targeted follow-up query → Still missing something? → Another query

4. Self-Reflection¶

The agent evaluates its own answers

Questions the agent asks itself: - "Did I answer the full question?" - "Do I have enough evidence?" - "Are there contradictions I need to resolve?" - "Should I search for more recent information?"

5. Source Validation¶

The agent checks credibility and consistency

Agent might: - Cross-reference multiple sources - Prioritize peer-reviewed sources - Flag conflicting information - Note when sources are outdated

Architecture Comparison¶

Traditional RAG Architecture:¶

┌─────────────┐
│ User Query  │
└──────┬──────┘
       │
       ▼
┌─────────────────┐
│  Embed Query    │
└──────┬──────────┘
       │
       ▼
┌─────────────────┐
│ Retrieve Docs   │ (One-shot retrieval)
└──────┬──────────┘
       │
       ▼
┌─────────────────┐
│ Generate Answer │
└─────────────────┘

Agentic RAG Architecture:¶

┌─────────────┐
│ User Query  │
└──────┬──────┘
       │
       ▼
┌─────────────────┐
│  Agent Planner  │ ← Plans multi-step approach
└──────┬──────────┘
       │
       ▼
┌─────────────────────────────────┐
│     Reasoning Loop              │
│  ┌─────────────────────┐       │
│  │ 1. Choose Tool      │       │
│  │ 2. Execute Action   │◄──┐   │
│  │ 3. Evaluate Result  │   │   │
│  │ 4. Need more? ──────┘   │   │
│  └─────────────────────┘   │   │
│                             │   │
│  Tools Available:           │   │
│  • Semantic Search          │   │
│  • Keyword Search           │   │
│  • Web Search              │   │
│  • SQL Query               │   │
│  • Calculator              │   │
└─────────────┬───────────────┘
              │
              ▼
     ┌─────────────────┐
     │ Synthesize &    │
     │ Generate Answer │
     └─────────────────┘

Key Advantages of Agentic RAG¶

1. Handles Complex Queries¶

Can break down multi-part questions and answer them systematically

Example: - Question: "What are the pros and cons of our competitor's pricing strategy, and how should we respond?" - Agent: Searches for competitor pricing → Analyzes pros/cons → Searches for our pricing → Formulates recommendations

2. Reduces Hallucinations¶

Can verify its own answers and search for contradictory evidence

3. Adapts to Context¶

Chooses different strategies based on the question type

Factual question → Quick keyword search
Analytical question → Multiple searches + synthesis
Recent events → Web search first
Technical question → Look in specific documentation

4. Provides Better Transparency¶

Shows its reasoning process and which sources it used

5. Self-Corrects¶

If initial retrieval doesn't answer the question, it can try different approaches

Common Agentic RAG Patterns¶

Pattern 1: ReAct (Reasoning + Acting)¶

The agent alternates between reasoning about what to do next and taking actions

Thought: I need to find the latest sales data
Action: search_documents("Q4 2024 sales")
Observation: Found revenue numbers but not profit margins
Thought: I need profit data too
Action: search_documents("Q4 2024 profit margin")
Observation: Found profit margins
Thought: Now I can calculate the answer
Action: calculate(revenue, margin)
Answer: [Final response]

Pattern 2: Plan-and-Execute¶

Agent makes a complete plan first, then executes it

Plan:
  Step 1: Get historical data (2020-2024)
  Step 2: Get current quarter data
  Step 3: Calculate growth rate
  Step 4: Compare to industry average

Execute each step...

Pattern 3: Self-Ask¶

Agent generates and answers sub-questions

Main Question: "Why did our customer churn increase?"

Sub-Q1: What was the churn rate? → Search & Answer
Sub-Q2: When did it start increasing? → Search & Answer  
Sub-Q3: What changed around that time? → Search & Answer
Sub-Q4: What do customers say? → Search reviews → Answer

Final Answer: [Synthesized from all sub-answers]

Building a Simple Agentic RAG (Conceptual)¶

Here's a simplified Python-like pseudocode:

class AgenticRAG:
    def __init__(self):
        self.tools = {
            'search': self.search_documents,
            'web_search': self.search_web,
            'calculate': self.calculator
        }

    def answer_question(self, question):
        # 1. Plan
        plan = self.create_plan(question)

        # 2. Execute with reasoning loop
        context = []
        for step in plan:
            # Reason about what to do
            thought = self.reason(step, context)

            # Choose and use a tool
            tool = self.choose_tool(thought)
            result = self.tools[tool](step)

            # Evaluate if we have enough
            context.append(result)
            if self.evaluate_sufficiency(question, context):
                break

            # Otherwise, refine and continue
            plan = self.refine_plan(plan, context)

        # 3. Synthesize final answer
        answer = self.generate_answer(question, context)

        # 4. Verify answer quality
        if not self.verify_answer(answer, context):
            # Try a different approach
            return self.answer_question(question)  # Retry

        return answer

    def reason(self, step, context):
        """Think about what information we need"""
        return f"To complete {step}, I need to..."

    def choose_tool(self, thought):
        """Decide which tool to use"""
        if "recent" in thought:
            return 'web_search'
        elif "calculate" in thought:
            return 'calculate'
        else:
            return 'search'

    def evaluate_sufficiency(self, question, context):
        """Do we have enough information?"""
        # Check if all parts of question are covered
        return self.coverage_check(question, context) > 0.9

When to Use Agentic RAG vs. Basic RAG¶

Use Basic RAG when:¶

Simple factual lookups ("What is the capital of France?")
Single-source answers ("What does page 5 of the manual say?")
Speed is critical
Questions are straightforward

Use Agentic RAG when:¶

Complex, multi-part questions
Comparative analysis needed
Answer requires synthesis from multiple sources
Query is ambiguous and needs clarification
Need to verify conflicting information
Research-style questions

Real-World Applications¶

1. Customer Support¶

Agentic RAG can search FAQs, product docs, ticket history
Self-corrects if first answer doesn't match the issue
Escalates if it can't find solution

2. Research Assistant¶

Breaks down research questions
Searches multiple databases
Cross-references findings
Produces literature review

3. Legal Document Analysis¶

Searches case law, statutes, precedents
Identifies contradictions
Builds comprehensive legal argument

4. Business Intelligence¶

Queries multiple data sources
Performs calculations
Generates insights with supporting evidence

Challenges & Limitations¶

1. Latency¶

Multiple retrieval steps take more time than single retrieval

Solution: Balance between thoroughness and speed

2. Cost¶

More LLM calls = higher API costs

Solution: Set limits on reasoning iterations

3. Complexity¶

Harder to debug and predict behavior

Solution: Log agent's reasoning process

4. Looping¶

Agent might get stuck in repetitive searches

Solution: Set maximum iterations, detect loops

Future of Agentic RAG¶

Emerging trends: - Multi-agent systems (multiple specialized agents working together) - Memory systems (agents remember past interactions) - Fine-tuned models for specific reasoning tasks - Better evaluation metrics for agent performance - Integration with external APIs and tools

Key Takeaways¶

Basic RAG = Retrieve once, generate answer
Agentic RAG = Plan, retrieve iteratively, reason, verify, then answer
Agency means the AI makes autonomous decisions about how to find information
Best for complex questions requiring multi-step reasoning
Trade-off between answer quality and speed/cost

Practice Questions for Students¶

Scenario Analysis: Given the question "Compare our product reviews to competitors", describe how an agentic RAG system would approach this vs. basic RAG.
Design Challenge: Design an agentic RAG system for a medical diagnosis assistant. What tools would it need? What safety measures?
Critical Thinking: When might agentic RAG make things worse instead of better? What questions are better suited for basic RAG?
Implementation: Sketch out the agent's reasoning loop for: "Why did our website traffic drop last month?"

Additional Resources¶

LangChain: Popular framework for building agentic RAG systems
LlamaIndex: Another framework with built-in agent capabilities
Papers: "ReAct: Synergizing Reasoning and Acting in Language Models"
Tools: OpenAI Assistants API, Anthropic's Claude with tools