How to build research agent

Agentic-aiHow-ToBeginner · 4 min read

How to Build a Research Agent with Generative AI

To build a research agent, use a language model to generate queries and analyze responses, combined with retrieval tools to fetch relevant documents. Integrate these with a loop that refines questions and summarizes findings automatically.

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Syntax

A research agent typically involves these parts:

Query Generator: Creates questions or search prompts.
Retriever: Fetches relevant documents or data.
Reader/Analyzer: Processes and summarizes the retrieved information.
Loop Controller: Repeats the process to refine results.

Each part can be a function or module that works together.

python

def generate_query(topic):
    return f"Find latest research on {topic}"  # Query Generator

def retrieve_documents(query):
    # Simulate document retrieval
    return ["Doc1 about AI", "Doc2 about AI"]  # Retriever

def analyze_documents(docs):
    return "Summary of docs"  # Reader/Analyzer

def research_agent(topic, steps=3):
    for _ in range(steps):
        query = generate_query(topic)
        docs = retrieve_documents(query)
        summary = analyze_documents(docs)
    return summary  # Loop Controller

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Example

This example shows a simple research agent that generates a query, retrieves dummy documents, and summarizes them in a loop.

python

def generate_query(topic):
    return f"Find latest research on {topic}"

def retrieve_documents(query):
    return ["Document 1 about AI advancements", "Document 2 about AI applications"]

def analyze_documents(docs):
    return "; ".join(docs)  # Simple summary by joining docs

def research_agent(topic, steps=2):
    summary = ""
    for i in range(steps):
        query = generate_query(topic)
        docs = retrieve_documents(query)
        summary = analyze_documents(docs)
        print(f"Step {i+1} summary: {summary}")
    return summary

final_summary = research_agent("artificial intelligence")

Output

Step 1 summary: Document 1 about AI advancements; Document 2 about AI applications Step 2 summary: Document 1 about AI advancements; Document 2 about AI applications

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Common Pitfalls

Not refining queries can lead to repeated or irrelevant results.
Using static or dummy data instead of real retrieval limits usefulness.
Skipping the loop means no improvement or deeper insight over time.
Ignoring summarization quality can produce unclear outputs.

python

def retrieve_documents_wrong(query):
    return ["Same doc"]  # Always returns same document

def research_agent_fixed(topic, steps=2):
    for i in range(steps):
        query = f"Refined query {i} for {topic}"
        docs = [f"Doc {i} about {topic}"]  # Different docs each step
        summary = "; ".join(docs)
        print(f"Step {i+1} summary: {summary}")

research_agent_fixed("AI")

Output

Step 1 summary: Doc 0 about AI Step 2 summary: Doc 1 about AI

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Quick Reference

Tips for building a research agent:

Use a language model to generate and refine queries.
Connect to a document retriever like a search engine or database.
Implement a reader to summarize or extract key info.
Run a loop to improve results step-by-step.
Test with real data for best results.

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Key Takeaways

A research agent combines query generation, document retrieval, and summarization in a loop.

Refining queries over multiple steps improves the quality of research results.

Use real data sources for retrieval to get meaningful information.

Summarization helps condense large information into clear insights.

Avoid static data and no-loop designs to prevent poor research outcomes.