Combining retrieval with agent reasoning in Agentic AI

Introduction

Combining retrieval with agent reasoning helps an AI find useful information and then think about it to give better answers.

When you want an AI to answer questions using a large collection of documents.

When the AI needs to look up facts before making a decision.

When you want the AI to explain its reasoning based on retrieved information.

When the AI should combine memory search with problem solving.

When you want to improve AI answers by adding relevant context from data.

Syntax

Agentic AI

class RetrievalAgent:
    def __init__(self, retriever, reasoner):
        self.retriever = retriever
        self.reasoner = reasoner

    def answer(self, question):
        context = self.retriever.retrieve(question)
        answer = self.reasoner.think(question, context)
        return answer

The retriever finds relevant information based on the question.

The reasoner uses that information to create a thoughtful answer.

Examples

This example shows simple retriever and reasoner classes that work together.

Agentic AI

class SimpleRetriever:
    def retrieve(self, question):
        return "Information about " + question

class SimpleReasoner:
    def think(self, question, context):
        return f"Answer based on: {context}"

This shows what happens if the retriever finds no information.

Agentic AI

class EmptyRetriever:
    def retrieve(self, question):
        return ""

class ReasonerWithNoContext:
    def think(self, question, context):
        if not context:
            return "No info found, cannot answer."
        return f"Answer: {context}"

This example shows retrieval returning multiple pieces of info and reasoning combining them.

Agentic AI

class RetrieverWithMultipleResults:
    def retrieve(self, question):
        return ["Info1", "Info2", "Info3"]

class ReasonerCombiningResults:
    def think(self, question, context):
        combined = ", ".join(context)
        return f"Combined answer from: {combined}"

Sample Model

This program creates a simple agent that retrieves info from a small knowledge base and reasons to answer questions. It shows answers for known and unknown questions.

Agentic AI

class Retriever:
    def retrieve(self, question):
        knowledge_base = {
            "weather": "The weather is sunny.",
            "time": "It is 3 PM.",
            "location": "You are in New York."
        }
        return knowledge_base.get(question.lower(), "No info available.")

class Reasoner:
    def think(self, question, context):
        if context == "No info available.":
            return "Sorry, I don't have an answer."
        return f"Based on what I found: {context}"

class RetrievalAgent:
    def __init__(self, retriever, reasoner):
        self.retriever = retriever
        self.reasoner = reasoner

    def answer(self, question):
        context = self.retriever.retrieve(question)
        answer = self.reasoner.think(question, context)
        return answer

# Create agent
retriever = Retriever()
reasoner = Reasoner()
agent = RetrievalAgent(retriever, reasoner)

# Questions
questions = ["weather", "time", "food"]

for question in questions:
    print(f"Q: {question}")
    print(f"A: {agent.answer(question)}")
    print()

Important Notes

Retrieval helps the agent get relevant facts quickly.

Reasoning lets the agent think and explain answers using retrieved info.

Common mistake: skipping retrieval and guessing answers without facts.

Use retrieval + reasoning when you want accurate, explainable AI answers.

Summary

Combining retrieval with reasoning helps AI find and use information better.

Retriever finds facts; reasoner uses them to answer thoughtfully.

This approach improves AI accuracy and explanation.