Tutorial • Project
Build an AI Research Agent
Step-by-step guide to building an agent that researches and writes reports • 12 min read
A research agent is one of the most useful AI agent projects you can build. Instead of manually searching, reading and summarizing content, the agent can do it for you: it collects sources, extracts key points, then generates a structured report.
In this tutorial, you will build a simple “Research → Summarize → Report” agent workflow. This guide focuses on the agent design and steps, so you can implement it in any framework (LangChain, CrewAI, AutoGen or even a custom script).
What You Will Build
- A user enters a topic (example: “Agentic AI tools in 2026”)
- The agent searches and collects sources
- The agent summarizes the content
- The agent writes a final report (with sections + bullet points)
Agent Architecture (Simple)
Agent 1: Researcher
Finds sources, extracts important info, keeps notes.
Agent 2: Writer
Converts notes into a structured report with headings and summaries.
Step-by-Step Build
Step 1 — Define the Input & Output
Decide what the user will provide and what the agent must produce.
- Input: topic + optional keywords
- Output: report with sections (Overview, Key Findings, Tools, Risks, Conclusion)
Step 2 — Add a Search Tool
Your researcher agent needs a tool to find sources. This could be:
- Web search API
- RSS feeds / curated links
- Your own knowledge base (documents)
Collect at least 5–10 sources for a strong report.
Step 3 — Extract Notes (Don’t Copy-Paste)
After gathering sources, the agent should extract key points into notes. Notes should be short and factual.
- Keep 3–6 bullet points per source
- Store the source title + link for reference
- Remove repeated info
Step 4 — Summarize & Organize
Next, the agent groups notes into themes. Example themes:
- Current trends
- Top tools and frameworks
- Best practices
- Risks and limitations
Step 5 — Generate the Final Report
Your writer agent should output a clean, readable report. Use this format:
- Overview
- Key Findings
- Tools / Frameworks
- Challenges & Risks
- Conclusion + Next Steps
Bonus Upgrades (Make It Pro)
- Add citations: include links for every major claim
- Add memory: store user topics and past reports
- Add quality check: reviewer agent verifies facts
- Export: generate PDF or DOCX report
Final Thoughts
A research agent is a perfect portfolio project because it demonstrates real-world automation. Once you have the basic workflow working, you can enhance it with memory, citations and multi-agent review.
Next tutorial?
Learn how RAG gives your agents long-term memory and better answers.
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