How to Build a Domain-Specific AI Research Agent (Claude Code, Cursor, or Any AI Tool)
A 3-phase methodology that turns any AI coding tool into an autonomous researcher — demonstrated with pharmaceutical research, adaptable to any domain
Research takes too long. Not because the information isn't findable — it is. Because finding, organizing, verifying, and formatting it is a series of repetitive steps you end up doing every single time, for every single project.
I built a research agent that does all of that automatically. You give it a topic. It runs a full 3-phase workflow — gathering, gap-filling, synthesizing — and hands you a structured report with source verification and formatted deliverables. Research that used to take days completes in 20–30 minutes.
I originally built this in Cursor, but the methodology works in Claude Code or any AI coding tool that accepts instruction files as context. The core isn't the tool. It's the instruction system — a set of structured files you give the AI once, and it follows every time after. This is the principle behind how AI agents work more broadly: you program behavior, not prompts.
Hi, I'm Jenny 👋 I build AI systems and tools, then document exactly how I did it. AI builder behind VibeCoding.Builders and other products with hundreds of paying customers. See all my launches →
If you're new to Build to Launch, welcome! Here's what you might enjoy:
- 12 Claude Code Project Ideas (with Prompts) — this research pipeline is Project 11
- How to Build Your First Claude Code Project — start here if you haven't built with Claude Code before
- AI Agents for Everyone — the framework behind autonomous agents like this one
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What you'll go through with me:
- Why This Works — and Why Prompting Alone Doesn't — the key insight about instruction systems vs. ad-hoc prompts
- The 3-Phase Research Methodology — the framework that makes autonomous operation possible
- Part I: See It Work — The Full Demo 🔒 — dupilumab walkthrough, 5-minute setup, exact prompt
- Part II: Complete Setup Guide 🔒 — folder structure, package details, 3 prompt templates
- Part III: How the Methodology Actually Works 🔒 — each phase broken into sub-steps with decision trees
- Part IV: Adapting to Any Domain 🔒 — complete market research case study with templates
- Part V: Troubleshooting and Pro Tips 🔒 — 5 problems, optimization strategies, getting started checklist
Why This Matters
Research is time-consuming and repetitive. You search for information, organize it, verify sources, format outputs, and repeat the process dozens of times. What if an AI agent could do this entire workflow autonomously while you focus on analysis and decision-making?
This guide shows you how to build exactly that, using Cursor or Claude Code and a structured methodology that can research pharmaceutical compounds, analyze markets, investigate competitors, or explore any domain systematically.
What you'll accomplish:
- Transform any AI coding tool into an autonomous research agent in 15 minutes
- See a complete research project executed from start to finish
- Understand the 3-phase methodology that makes automation possible
- Adapt the system to market research or any other domain
- Handle common issues and optimize performance
This isn't about better prompting — it's about programming AI behavior through structured instruction systems.
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