How to Build Personalized AI Research Agent (with 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
Most AI research workflows are just better search. This is different: a structured 3-phase methodology that turns Claude Code, Cursor, or any AI coding tool into an autonomous research agent that runs start-to-finish without you. Demonstrated with pharmaceutical research (an $11B blockbuster drug) — adaptable to market research, competitive intelligence, legal research, or any domain. Full setup, the exact prompt, and the domain adaptation system are all here.
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.
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
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
The 3-Phase Research Methodology
The secret to autonomous research isn’t a smarter prompt. It’s giving the AI an operating manual — structured files it reads once and follows systematically every time.
Phase 1: Primary Data Collection
What the AI does automatically:
Creates standardized directory structure
Executes predefined search queries for different data types
Populates markdown templates with discovered information
Identifies preliminary data gaps
Key insight: Phase 1 casts a wide net to establish the research landscape before diving deep.
Phase 2: Gap Analysis & Targeted Research
What the AI does automatically:
Analyzes all Phase 1 findings for completeness
Executes specific searches for identified gaps
Verifies source credibility using built-in standards
Updates research files with new discoveries
Key insight: Phase 2 transforms broad research into comprehensive, high-quality analysis.
Phase 3: Data Synthesis
What the AI does automatically:
Synthesizes findings into coherent narratives
Creates Python script for Excel generation
Performs final quality assurance
Documents any remaining limitations or gaps
Key insight: Phase 3 transforms raw research into professional deliverables ready for stakeholder presentation.
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The methodology above is the framework. Here’s the full implementation:
Part I: The full demo — dupilumab ($11B blockbuster drug) researched from scratch: the 5-minute setup, the exact prompt, the complete autonomous workflow step by step, and the final output package
Part II: Complete setup guide — system requirements, folder structure diagrams, package structure with every file explained, and 3 ready-to-use prompt templates (basic research, update existing, generate report only)
Part III: How the methodology actually works — each phase broken into 3 sub-steps, the decision trees the AI uses for source evaluation, how it makes intelligent choices at each step
Part IV: Adapting to any domain — a complete market research case study (Tesla competitive analysis) with actual search query templates and Excel specs, plus legal, academic, and competitive intelligence adaptation guides
Part V: Troubleshooting and pro tips — 5 common problems with specific solutions, optimization strategies, advanced multi-project techniques, and a getting-started checklist for the next 30 minutes / this week / this month
Plus: the self-audit prompt that makes any AI tool reflect on its own research output and flag what it missed — the single most useful addition to any research workflow.