A comprehensive guide to building reliable, cost-effective AI agents in production
Building AI agents is hard. Current resources are scattered across blog posts, framework docs, and Twitter threads. This guide consolidates proven patterns, trade-offs, and production lessons into one place.
Who this is for:
- Developers building AI-powered applications
- Architects designing agent systems
- Teams taking agents from prototype to production
New to AI agents? Start here:
- What is an Agent? - Understand the fundamentals
- Decision Tree - Find the right pattern for your use case
- Terminology - Learn the vocabulary
Ready to build? Jump to Core Patterns
Going to production? Check Production Engineering
Deep dives into agent architectures:
- Tool Calling - Foundational pattern for LLM function execution
- ReAct (Reasoning + Acting) - Iterative reasoning and action loops
- Chain-of-Thought - Step-by-step explicit reasoning
- Sequential Chain - Linear multi-step workflows
- Parallel Execution - Concurrent task processing
- Router Agent - Dynamic task routing to specialists
- Hierarchical Agents - Manager-worker coordination
- Feedback Loop - Self-improving iterative refinement
Taking agents to production:
- Memory Architectures - Short-term, long-term, and hybrid memory systems
- Error Handling - Retries, circuit breakers, graceful degradation
- Observability - Logging, tracing, metrics, and debugging
- Cost Optimization - Model selection, caching, and token efficiency
- Rate Limiting - API quotas, queuing, and backpressure
- Security - Prompt injection defense, PII protection, sandboxing
- Testing Strategies - Unit tests, evaluation frameworks, regression testing
Choosing the right tools and approaches:
- LangChain vs LlamaIndex vs Custom - Feature matrix, cost analysis, migration paths
- OpenAI Assistants vs Custom Agents - Managed service vs self-hosted tradeoffs
- Synchronous vs Asynchronous Execution - Performance and scalability implications
Production implementations with metrics:
- Customer Support Agent - Router + hierarchical pattern, 98% cost reduction
- Code Review Agent - Sequential chain + feedback loop, 85% issue detection
- Research Assistant Agent - Hierarchical + parallel execution, 90% time savings
- Data Analyst Agent - Tool calling + chain-of-thought, SQL generation from natural language
Essential references and community:
- Research Papers - 20+ foundational papers (ReAct, Chain-of-Thought, Toolformer, etc.)
- Tools & Frameworks - LangChain, LlamaIndex, vector databases, deployment platforms
- Communities - Discord servers, newsletters, learning paths, conferences
Developers: Start with the Decision Tree, pick a pattern, implement it, then review Production Engineering.
Architects: Review Framework Comparisons, study Case Studies, then design using Core Patterns.
Product Managers: Read What is an Agent? and Case Studies to understand capabilities and constraints.
Researchers: Explore Research Papers and follow the Communities.
- "I need to build X" → Decision Tree
- "Show me how it works" → Core Patterns
- "What are the trade-offs?" → Framework Comparisons
- "How do I deploy this?" → Production Engineering
- "Prove it works" → Case Studies
- "What tools should I use?" → Tools & Frameworks
- "Where can I learn more?" → Communities
This is a living document. If you've built production agents and have lessons to share, contributions are welcome!
See CONTRIBUTING.md for guidelines on:
- Submitting new patterns or case studies
- Updating existing content
- Reporting issues
- Style guide and standards
Version: 1.0.0 (January 2026) Status: ✅ Production-ready documentation Updates: See CHANGELOG.md
Stats:
- 📄 30+ comprehensive guides
- 💻 100+ production code examples
- 📊 25+ architecture diagrams
- 💰 Real cost analyses and ROI calculations
- 🏆 4 complete case studies with metrics
MIT License - Use this knowledge to build great things.
⭐ Star this repo if it helps you build better AI agents. 🔗 Share it with your team and community. 🤝 Contribute your production learnings.