AI Agent Patterns & Architecture
A comprehensive guide to building reliable, cost-effective AI agents in production
Why This Guide?
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
Quick Start
New to AI agents? Start here:
- What is an Agent? - Understand the fundamentals
- [Decision Tree](https://github.com/devwithmohit/ai-agent-architecture-p…
AI Agent Patterns & Architecture
A comprehensive guide to building reliable, cost-effective AI agents in production
Why This Guide?
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
Quick Start
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
Documentation Structure
🎯 Foundation & Decision Framework
🔧 Core Patterns
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
🚀 Production Engineering
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
📊 Framework Comparisons
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
🏗️ Real-World Case Studies
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
📚 Resources
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
How to Use This Guide
By Role
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.
By Goal
- "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
Contributing
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
Project Status
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
License
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.