How to Build Deepresearch: The Production-Grade Blueprint

Building a Deep Research agent-an autonomous system that plans, executes, reads, and synthesizes information from the web-is the single most requested capability in enterprise AI right now.

It differs fundamentally from RAG. RAG is static (looking up what you already know). Deep Research is dynamic (going out to find what you don’t know).

Most developers hack this together with a simple while loop and a search tool. That works for a demo. In production, that architecture leads to infinite loops, hallucinated citations, and $500 API bills in a single afternoon.

Here is the architectural pattern I use to deploy robust Deep Research agents for enterprise clients.

🏗️ The Architecture

We are not building a chatbot; we are building a state machine. The system must be able to fork (parallelize research), join (synthesize findings), and recurse (dig deeper based on gaps).

The “Plan-and-Execute” Pattern

1. The Planner (Supervisor): Decomposes the user’s vague request (“Competitor analysis of Stripe”) into a structured Directed Acyclic Graph (DAG) of sub-questions.
2. The Researchers (Workers): Independent agents that take a sub-question, execute search queries, scrape content, and summarize findings. These run in parallel.
3. The Critic (Reflection): Evaluates the gathered data. Does it answer the original question? If no, it routes back to the Planner with specific gaps to fill.
4. The Writer: Synthesizes the final report with strict citation enforcement.

🛠️ The Stack

• Orchestration: LangGraph (Python). This is non-negotiable in 2025. You need graph-based state management, not simple chains.
• Search & Extraction: Tavily API. Google Search API is too messy. Tavily returns clean, parsed context optimized for LLMs.
• Model: GPT-4o or Claude 3.5 Sonnet. You need high reasoning capabilities for the planning phase.
• Validation: Pydantic. Structured output is required to prevent the agent from “chatting” when it should be returning data.

💻 Implementation

This implementation uses LangGraph’s Send API to handle parallel execution of research tasks-a critical feature for speed.

Prerequisites

pip install langgraph langchain-openai tavily-python pydantic

The Core Logic

import operator
from typing import Annotated, List, TypedDict
from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage
from langgraph.graph import StateGraph, END
from langgraph.constants import Send
from pydantic import BaseModel, Field
from tavily import TavilyClient
import os

# --- Configuration ---
# In production, load these from secure env vars
TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

tavily = TavilyClient(api_key=TAVILY_API_KEY)
llm = ChatOpenAI(model="gpt-4o", temperature=0)

# --- State Definitions ---

class ResearchStep(BaseModel):
    query: str = Field(description="The specific search query to execute")
    rationale: str = Field(description="Why this query is necessary")

class ResearchPlan(BaseModel):
    steps: List[ResearchStep] = Field(description="List of research steps to execute")

class ResearchResult(BaseModel):
    query: str
    content: str
    source_url: str

# The Global Graph State
class AgentState(TypedDict):
    original_task: str
    plan: List[ResearchStep]
    results: Annotated[List[ResearchResult], operator.add] # Append-only list
    final_report: str
    iteration: int

# --- Nodes ---

def planner_node(state: AgentState):
    """Generates a research plan based on the user request."""
    print(f"--- Planning: Iteration {state.get('iteration', 0)} ---")

    # Enforce structured output for reliable parsing
    planner = llm.with_structured_output(ResearchPlan)

    system_prompt = (
        "You are a Senior Research Lead. Break down the user's request into "
        "3 distinct, actionable search queries. "
        "Focus on finding factual data."
    )

    # If we are iterating, look at previous results to refine the plan
    context = ""
    if state.get("results"):
        context = f"Previous findings: {str(state['results'])}\nFocus on MISSING information."

    response = planner.invoke([
        SystemMessage(content=system_prompt),
        HumanMessage(content=f"Task: {state['original_task']}\n{context}")
    ])

    return {"plan": response.steps, "iteration": state.get("iteration", 0) + 1}

def research_worker_node(step: ResearchStep):
    """Executes a single research step. Runs in parallel."""
    print(f"--- Executing Search: {step.query} ---")

    try:
        # Tavily handles search + scraping in one call
        search_result = tavily.search(query=step.query, search_depth="advanced", max_results=2)

        # Combine content from results
        content = "\n".join([r['content'] for r in search_result['results']])
        url = search_result['results'][0]['url'] if search_result['results'] else "N/A"

        return {"results": [ResearchResult(query=step.query, content=content, source_url=url)]}
    except Exception as e:
        # Fail gracefully in production
        return {"results": [ResearchResult(query=step.query, content=f"Error: {str(e)}", source_url="Error")]}

def synthesizer_node(state: AgentState):
    """Compiles all gathered info into a final answer."""
    print("--- Synthesizing Report ---")

    all_data = "\n\n".join([f"Source: {r.source_url}\nData: {r.content}" for r in state['results']])

    messages = [
        SystemMessage(content="You are an expert analyst. Write a concise summary based ONLY on the provided context."),
        HumanMessage(content=f"Original Request: {state['original_task']}\n\nContext:\n{all_data}")
    ]

    response = llm.invoke(messages)
    return {"final_report": response.content}

def router_node(state: AgentState):
    """Decides whether to recurse or finish."""
    # Production Logic: Hard limit on iterations to prevent infinite costs
    if state["iteration"] > 2:
        return "synthesize"

    # Simple check: Do we have results?
    # In a real app, use an LLM "Critic" node to evaluate data quality here.
    if state.get("results"):
        return "synthesize"

    return "synthesize"

# --- Graph Construction ---

workflow = StateGraph(AgentState)

workflow.add_node("planner", planner_node)
workflow.add_node("research_worker", research_worker_node)
workflow.add_node("synthesizer", synthesizer_node)

# Entry point
workflow.set_entry_point("planner")

# Conditional Edge: Map the plan to parallel workers
def map_research_steps(state: AgentState):
    # This uses the Send API to create parallel branches
    return [Send("research_worker", step) for step in state["plan"]]

workflow.add_conditional_edges("planner", map_research_steps)

# Workers return to a "wait" state or directly to router?
# In LangGraph, map-reduce usually collects back to a reducer.
# We connect workers to the synthesizer (or a router if we were looping).
workflow.add_edge("research_worker", "synthesizer")
workflow.add_edge("synthesizer", END)

# Compile
app = workflow.compile()

# --- Execution ---

if __name__ == "__main__":
    inputs = {
        "original_task": "What are the latest pricing changes for AWS Lambda in 2025?",
        "iteration": 0,
        "results": []
    }

    for event in app.stream(inputs):
        for key, value in event.items():
            print(f"Finished Node: {key}")

    print("\n\n=== FINAL REPORT ===\n")
    # Access the final state from the last event or by invoking
    final_state = app.invoke(inputs)
    print(final_state["final_report"])

⚠️ Production Pitfalls (The “Senior” Perspective)

When you move this from a notebook to a system serving 10k users, here is what breaks:

1. The Context Window Explosion

Deep research gathers massive amounts of text. If you blindly append every scraped page to the results list, you will hit the 128k token limit of GPT-4o very quickly.

• Fix: Implement a Summarization Step inside the research_worker_node. Do not return raw HTML. Return a compressed 200-token summary of the finding relevant to the query.

2. The “Rabbit Hole” Loop

Agents love to procrastinate. If you give them a generic “Is this enough info?” check, they will often say “No, I need to verify X” forever.

• Fix: Hard-code a MAX_ITERATIONS constant (e.g., 3). Never let the model decide when to stop completely on its own without a failsafe.

3. Rate Limiting

Spinning up 10 parallel search workers sounds great until Tavily or OpenAI rate-limits you (429 Errors).

• Fix: Use a semaphore or a token bucket algorithm to control the concurrency of the Send API.

4. Hallucinated URLs

Agents will sometimes invent sources if the synthesis prompt is weak.

• Fix: In the synthesizer_node, provide the content as a dictionary {ID: URL} and force the LLM to reference data by ID. Then, programmatically replace IDs with the actual URLs in the final string.

🚀 Final Verdict

This architecture-Planner -> Parallel Workers -> Synthesizer-is the industry standard for Deep Research in 2026. It balances speed (parallelism) with quality (iterative planning).

If you are building an MVP, use the code above. If you are building for enterprise, you need to add a persistence layer (Postgres) to LangGraph to allow users to “pause” and “resume” research sessions.