Building a Deep Research Agent: An End-to-End Walkthrough

The Problem with LLMs as Researchers

If you ask ChatGPT to "research the current state of solid-state batteries," it gives you a decent overview. But if you ask it for a citation-backed technical brief with recent breakthroughs from the last month, it fails. It hallucinates papers, mixes up dates, and speaks in generalities.

As builders, we know why: LLMs are reasoning engines, not knowledge bases.

To build a reliable research agent, we can't rely on the model's training data. We need to give it tools to go outside, fetch real-time data, and—most importantly—force it to cite its sources. In this walkthrough, I’m going to share the architecture and code logic for a research agent I use to automate technical briefs.

The Architecture: The Research Loop

A linear chain (Input → Search → Output) isn't enough for deep research. We need a loop. My architecture looks like this:

1. Query Analyzer: Breaks the user's vague request into specific search queries.
2. The Hunter (Search & Scrape): Executes searches and scrapes content.
3. The Filter: Discards irrelevant content to save context tokens.
4. The Synthesizer: Compiles the data, checking for hallucinations.
5. The Architect: Formats the output into a specific JSON schema (Brief, Action List, Sources).

---

Step 1: The Stack

For this build, we are keeping it lean. You don't need a massive framework, but you do need specific tools:

• Orchestration: Python (LangChain or raw OpenAI API).
• Search Tool: Tavily API. I prefer Tavily over Google Search API because it returns clean context, not just snippets, and handles the scraping for us.
• Model: GPT-4o or Claude 3.5 Sonnet. You need high reasoning capabilities for the synthesis step.

Step 2: breaking Down the Query

Users ask lazy questions. "Tell me about AI agents." If you search that verbatim, you get generic SEO spam. We need an agent step that expands this into search terms.

def generate_search_queries(topic: str):
    prompt = f"""
    You are a research planner. Break down the topic '{topic}' into 3 distinct search queries 
    optimized for a search engine.
    1. General overview
    2. Technical implementation details
    3. Recent news/competitor analysis
    
    Return strictly a JSON list of strings.
    """
    # ... Call LLM ...
    return json_queries

For "AI Agents," this generates: "AI agent architecture patterns," "LangGraph vs AutoGen comparison," and "Future of autonomous agents 2024." Now we have a roadmap.

Step 3: The Search & Context Layer

This is where most research agents fail. They pull top 10 Google results and dump the HTML into the context window. This creates noise.

Using Tavily, we can get raw text content. The key here is Citation Discipline. We need to store the URL alongside the content chunk so the LLM knows exactly where a fact came from.

from tavily import TavilyClient

tavily = TavilyClient(api_key="tvly-...")

def search_and_pack(queries):
    context_buffer = []
    for query in queries:
        # search_depth="advanced" gives us full text content, not just snippets
        response = tavily.search(query=query, search_depth="advanced", max_results=3)
        
        for result in response['results']:
            context_buffer.append(f"Source: {result['url']}\nContent: {result['content']}\n---")
            
    return "\n".join(context_buffer)

Pro-tip: If you are building for production, implement a re-ranking step here. Use a lightweight embedding model to score the relevance of the search results against the original query before feeding them to the expensive LLM.

Step 4: Synthesis & Hallucination Avoidance

Now we have a massive block of text with sources. We need to synthesize it. The prompt engineering here is critical.

We must use a "Citations Required" constraint. If the model states a fact, it must append [Source URL].

The System Prompt:

You are a technical analyst. You will write a research brief based ONLY on the provided context.

RULES:
1. Citation Discipline: Every claim must be immediately followed by the source URL from the context. 
2. No Hallucinations: If the context does not contain the answer, state "Data unavailable."
3. Tone: Concise, technical, builder-centric.

Step 5: Structured Output (The Action List)

A wall of text is hard to act on. I always force my agents to output structured JSON using Pydantic or OpenAI's Function Calling. This allows me to render the research into a nice UI later or pipe it into a Notion database.

from pydantic import BaseModel, Field
from typing import List

class ResearchBrief(BaseModel):
    summary: str = Field(..., description="Executive summary of findings")
    key_insights: List[str] = Field(..., description="Bullet points of technical details")
    action_items: List[str] = Field(..., description="Suggested next steps for a developer")
    sources: List[str] = Field(..., description="List of unique URLs used")

# Utilizing OpenAI's structured output
completion = client.beta.chat.completions.parse(
    model="gpt-4o-2024-08-06",
    messages=[...],
    response_format=ResearchBrief,
)

The Final Output

When you run this pipeline, you don't just get a chat response. You get an object. Here is what the "Action List" looks like when researching "Vector Databases":

• Evaluate Pinecone serverless vs. Milvus for cost at scale.
• Review the impact of HNSW indexing on query latency.
• Prototype a hybrid search pipeline using sparse-dense vectors.

This is actionable. It moves you from "learning" to "building."

Conclusion: Next Steps

This is a V1 research agent. It works well for linear queries. To take this to the next level (V2), we would introduce recursive logic (often called "multi-hop" reasoning). If the agent reads a source that mentions a technology it doesn't understand, it should pause, trigger a new search for that term, learn it, and then resume the original synthesis.

That is where frameworks like LangGraph shine, managing the state between these hops. But start here. Get the citation discipline right first, or your complex agent will just be a complex hallucination machine.