AI Agent Engineering Guides: Architecture, Tools, Evaluation, and Recovery
16 posts in this series
Use this series when you want to build agents that are more than demos. It follows the engineering path from safe execution and core architecture to memory, tool calling, LangGraph-style orchestration, evaluation, and production recovery.
Agent Sandbox Guide: A Complete Solution for Safely Running AI Code
A comprehensive guide to building AI Agent sandbox environments, comparing gVisor and Firecracker technologies with deployment guides from local development to Kubernetes clusters
AI Agent Development in Practice: Architecture Design and Implementation Guide
Deep dive into AI Agent architecture design: comparison of ReAct, Plan-and-Execute, and Multi-Agent patterns, five multi-agent orchestration patterns explained, with Claude Agent SDK practical code examples to help you master from theory to practice.
Agent Memory System Design: From Session to Long-Term Memory
Building an Agent memory system from scratch: Four memory types selection, five-stage pipeline implementation, Mem0/Zep/LangMem framework comparison, and production-grade cost optimization strategies
AI Agent Memory Management: Long-term Memory and Knowledge Governance in Practice
A deep dive into AI Agent memory systems: three memory types, four-layer cognitive architecture, and comparison of six major frameworks. From Mem0 to Letta, from vector databases to knowledge graphs—solving Agent memory loss and context decay issues.
Agent Tool Calling in Practice: Let AI Call External APIs and Services
From Function Calling to MCP, a deep dive into Claude and OpenAI's tool calling mechanisms with complete code examples and best practices to build AI Agents with API calling capabilities
Computer-Use Agent: Let AI Operate Your Computer
A comprehensive guide to Claude Computer Use technology, from principles to practice. Includes Docker deployment, code examples, competitor analysis, and security best practices for AI desktop automation.
Multi-Agent Collaboration in Practice: A Guide to 4 Architecture Patterns
Master the 4 core architecture patterns for multi-agent collaboration systems, from Subagents to Router, with LangGraph code implementations and production-grade performance optimization tips.
AI Agent Toolchain Design: From Single Tools to Tool Ecosystems - A 2026 Guide
Complete guide to AI Agent toolchain design: MCP protocol, framework selection (LangChain, CrewAI, AutoGen), evolution path from single tools to ecosystems, and enterprise deployment case studies.
LangGraph State Management: Checkpoints, Thread State, and Failure Recovery
A 2026 LangGraph state management guide covering checkpoints, thread state, failure recovery, AutoGen comparison, and monitoring patterns for production agents.
LangGraph Multi-Agent Collaboration in Practice: Supervisor Pattern and Task Dispatch
Deep dive into LangGraph Supervisor pattern architecture, master multi-agent task dispatch and collaboration through a Research + Writing team case study, with complete runnable code examples
LangGraph vs AutoGen State Tracking: Checkpoint Mechanisms, Timeout Recovery, and Framework Selection
Deep comparison of LangGraph vs AutoGen state tracking: 12-dimension quantitative analysis covering checkpoint mechanisms, timeout recovery, and distributed support. Includes real-world pitfalls, decision trees, and runnable code to help you choose the right framework
LLM Structured Outputs: JSON Schema Enforcement and Tool Calling Reliability Assurance
A comprehensive guide to production-grade LLM structured outputs: from JSON Schema enforcement validation to tool calling reliability assurance. Compare OpenAI, Claude, and Gemini implementations, with Python/TypeScript production templates and a three-layer reliability architecture for 100% format compliance.
Agent Evaluation Benchmarks in Practice: A Performance Testing Guide from AgentBench to DeepEval
A comprehensive guide to Agent evaluation benchmarks and performance testing frameworks, comparing five major benchmarks including AgentBench, WebArena, and τ-Bench, with DeepEval component-level evaluation methods and complete code examples.
How to Evaluate Agent Planning Capabilities: A Practical Guide to Reasoning Depth, Task Decomposition, and Self-Correction Testing
How do you evaluate Agent planning capabilities? This article details evaluation methodologies for reasoning depth, task decomposition, and self-correction, compares mainstream benchmarks like AgentBench, ToolBench, and ACPBench, and provides a practical evaluation guide.
AI Agent Monitoring and Recovery: From Logs to State Machines
AI Agents failing in production with no way to debug? This complete guide covers structured logging, metrics, OpenTelemetry tracing, and state machine patterns for production-ready monitoring.
DeepAgents Architecture: Planning Tools, Sub-agents, and File System
Deep dive into DeepAgents' four-pillar architecture: Planning Tools, Sub-agents, File System, and System Prompts. Compare with LangGraph, AutoGen, and other frameworks. Includes practical code examples and best practices.
