How to Build a Deep Research Agent
For Research teams building autonomous research agents · Based on Krish Naik Agentic AI Stack Builder
// TL;DR
This use-case guides research teams through building a Deep Research Agent that autonomously researches complex topics across multiple sources. Using LangGraph, you'll create a planning node that decomposes a question into sub-questions, a retrieval node that searches per sub-question, a synthesis node that combines findings, and a gap-detection conditional edge that loops until coverage is sufficient. Agentic RAG makes retrieval a tool the agent calls autonomously, and correctness plus hallucination-rate Evals validate output against a gold-standard test set before you trust the findings.
What is a Deep Research Agent?
A Deep Research Agent is a multi-step agentic system that decomposes a complex question into sub-questions, iteratively retrieves and synthesises information, detects knowledge gaps, and loops until the research goal is satisfied. Unlike a single retrieval pass, it performs multi-hop research — chasing follow-up questions the way a human researcher does. This is built on LangGraph, the orchestration framework for stateful, multi-step workflows.
How do you structure the agent in LangGraph?
LangGraph lets you define nodes (processing steps), edges (transitions), and conditional edges (routing decisions). For a research agent, build:
- A planning node that decomposes the user's question into sub-questions.
- A retrieval node that runs a search tool per sub-question.
- A synthesis node that combines findings into a coherent answer.
- A gap-detection conditional edge that checks coverage and loops retrieval until the research goal is met, then stops.
This stateful loop is what separates a research agent from a one-shot RAG query.
How do you wire retrieval as a tool?
Use Agentic RAG — wrap retrieval as an agent tool so the LLM decides when and how many times to retrieve, rather than retrieval being a fixed pipeline step. Define your search functions with the @tool decorator:
```python
from langchain.tools import tool
@tool
def web_search(query: str) -> str:
"""Search the web for current information on a query."""
...
@tool
def academic_db_search(query: str) -> str:
"""Search an academic database for peer-reviewed sources on a query."""
...
```
The docstrings are the schemas the LLM uses to choose the right search source, so make them precise — web_search versus academic_db_search must be clearly distinguishable.
Which model and setup should you use?
Initialise with `init_chat_model` so you can swap providers freely — a research agent may benefit from a stronger reasoning model for the planning node and a faster one for retrieval synthesis. Set up the environment with UV (`uv init`, `uv venv`, `uv add -r requirements.txt`) and store API keys in a `.env` file. Use structured output with a Pydantic model when the synthesis node must return findings in a parseable format for downstream reporting.
How do you know the research is trustworthy?
This is where Evals are non-negotiable. Run correctness and hallucination-rate Evals against a gold-standard test set before trusting the agent's output. Faithfulness Evals confirm the synthesis matches retrieved sources rather than inventing citations. A Deep Research Agent that hasn't been evaluated is a confident-sounding liability.
How do you keep it safe and observable?
Add Guardrails to keep the agent on its research domain and block malformed inputs. As you scale, an LLM Gateway handles routing between models, rate limiting, cost tracking, and fallback logic — important when a single research run fires many retrieval and synthesis calls.
Next step
Prototype the four-node LangGraph loop first with a single search tool, confirm the gap-detection edge loops correctly, then layer in Agentic RAG tools and correctness Evals before deploying against real research questions.
// FREQUENTLY ASKED QUESTIONS
Why use LangGraph instead of create_agent for research?
Research requires complex control flow — looping retrieval, branching on knowledge gaps, and stateful synthesis across multiple hops. LangGraph defines nodes, edges, and conditional edges explicitly, making it the right framework for iterative Deep Research Agents. create_agent suits single-pass tasks with a tool set, but it can't express the loop-until-satisfied logic a research agent needs.
How does the agent decide when to stop researching?
You implement a gap-detection conditional edge in LangGraph that checks whether the retrieved and synthesised information sufficiently covers the sub-questions. If gaps remain, the edge routes back to the retrieval node for another hop; if coverage is complete, it routes to the final synthesis and stops. This stopping condition prevents infinite loops and wasted API calls.
How do I prevent the research agent from fabricating sources?
Run hallucination-rate and faithfulness Evals against a gold-standard test set before trusting output — faithfulness confirms the synthesis matches retrieved context. Use Agentic RAG so answers are grounded in actual retrieved documents, and add Guardrails to enforce domain boundaries. Evals are the quality gate; never deploy a research agent that hasn't been evaluated for correctness.