AI Agent Systems
Build Intelligent Systems That Work While You Think
Welcome to AI Agent Systems, where you learn to build autonomous agents and intelligent workflows that scale your expertise without scaling your workload.
If youâre ready to go beyond automation and create AI systems that think, decide, and execute independently, youâre in the right place.
What Youâll Find Here:
Complete implementation guides for building autonomous research agents, content generation systems, and knowledge retrieval agents from scratch
Production-ready code examples and system architectures for RAG, MCP, and multi-agent orchestration
Real case studies from shipped applications: note generators, research systems, and data-driven tools serving real users
Step-by-step frameworks for turning your domain expertise into AI agents that work independently while you focus on strategy
Hi, Iâm Jenny đ
I build AI systems that ship to production. Creator of Quick Viral Notes, Substack Explorer, Vibe Coding Builders, several apps and autonomous research agents. Each week, I share practical guides for building intelligent systems that work autonomously. If youâre new here - welcome!
Build Your First Agent
Start with these foundational systems, each designed to work independently:
đ Build a Content Generation Agent
From prototype to production: How to build an AI agent that generates engaging content from your long-form work.
Generate 18 variations per article automatically
Deploy to production and serve real users
Handle async processing and credit management
đ§ Build an Email Processing Agent
Complete system for autonomous email subscription summarization and recommendation.
Connect to Gmail API and extract content automatically
Build scoring systems for content quality and relevance
Generate summaries and prioritize reading
đ Build Data-Driven Applications
Turn your data collection into production apps that serve insights autonomously.
Design database-backed analytics systems
Build real-time dashboards with automated updates
Deploy with Next.js and Vercel for scalability
đ Orchestrate Multiple AI Agents
Learn to manage AI agents like a production team - assign roles, manage handoffs, build feedback loops.
Cast different models for different roles (Claude for code, GPT for docs, Gemini for QA)
Build multi-step research workflows with checkpoints
Avoid the AI Nanny Syndrome with structured supervision
Advanced Agent Systems
Once youâve built your first agent, scale to production-ready multi-agent systems:
đŹ Build an Autonomous Research Agent
Complete guide to building domain-specific research agents that autonomously gather data and generate professional reports.
Set up autonomous pharmaceutical research system in 15 minutes
Adapt the framework to market research or competitive analysis
Build 3-phase methodology for any research domain
đ§ Build Your Second Brain with RAG
Build a RAG system that understands your work and surfaces insights instantly without manual searching.
Set up semantic search across your content
Create chunking strategies that preserve meaning
Build chat interfaces with source citations
đ Connect AI to Your Data with MCP
Build custom MCP servers that connect AI directly to your databases and knowledge sources.
Create your first MCP for database queries
Connect Claude Desktop to your data sources
Eliminate manual SQL and data retrieval friction
Frequently Asked Questions
Whatâs the difference between automation and AI agent systems?
Automation executes predefined sequences (if this, then that). AI agent systems think, decide, and adapt independently based on your goals.
Traditional automation: You set up the sequence â it executes the same way every time
AI agent systems: You define the goal â AI figures out how to achieve it and adapts based on context
Do I need to be a developer to build these systems?
You need curiosity and willingness to learn. I didnât have a CS degree when I started. These guides assume youâre learning as you build, with clear explanations of technical concepts and production-ready code you can adapt.
What tools and technologies do you use?
The guides cover practical stacks: Python for agents, Next.js for web apps, RAG with embeddings, MCP for data connections, Cursor for AI-assisted development. Youâll learn through building real systems, not theoretical examples.
How long does it take to build an autonomous agent?
Your first research agent: 15 minutes to set up and test. A production content generation app: 2-3 weeks from concept to deployment. The frameworks are designed to accelerate each subsequent build.
Can these systems work with my specific domain or data?
Yes. The frameworks are domain-agnostic. Youâll learn to adapt research agents from pharmaceutical to market analysis, or build custom MCP servers for your specific databases. The methodology transfers across any knowledge domain.
What if I get stuck or need help?
Each guide includes troubleshooting sections with real problems and solutions. The Build to Launch community has builders solving similar challenges. You can also reach out directly through comments.
Whatâs the difference between free and premium resources?
Free guides provide complete implementation details and working code. Premium resources include workshop recordings, advanced prompt systems, detailed architecture templates, and priority support for your specific use cases.
Start Building Your First Agent
đ New to AI agents? Start with Building Your Second Brain with RAG - itâs the simplest entry point and immediately useful.
đ Want to build research systems? Jump into The Complete Guide to Building Domain-Specific AI Research Agents, youâll have a working agent in 15 minutes.
đ Ready to ship production apps? Follow the journey from prototype to production deployment with real code, real challenges, real solutions.
đ Need systematic frameworks first? Learn The 3-Mode AI Workflow System to eliminate tool paralysis before diving into building.
đ Building something cool? Share it in the Vibe Coding Builders platform and get featured in the Friday series.
The future isnât about using AI tools, itâs about building AI systems that understand your domain, scale your expertise, and work autonomously while you focus on strategy and growth.
These arenât theoretical concepts. Theyâre production systems serving real users, with complete implementation guides so you can build your own.
Updated January 2026