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06-24

15 AI Agent Design Patterns Every Engineer Must Know

This article outlines 15 AI agent design patterns—ranging from single agent, sequential/parallel multi-agent, review/refinement loops, coordinator, hierarchical decomposition, ReAct, human-in-the-loop, plan-and-execute, reflexion, custom logic, to event-driven agents. Each pattern comes with real-world examples, clear when-to-use and when-it-breaks heuristics, and a decision matrix based on the type of uncertainty. The author emphasizes starting with the simplest pattern, capping iterations and costs, logging full action traces, and keeping irreversible actions behind deterministic checks or human approval. A practical guide for engineers building production agentic systems.

06-12

How To Build AI Agents in 2026 (That Actually Work)

This article systematically deconstructs the architecture and engineering practices for building practical AI agents. It clarifies the boundaries between chatbots, AI agents, and agentic AI, emphasizing that a real agent is a system that persistently loops toward a goal rather than delivering a one-shot answer. The author explains the ReAct loop (Reasoning + Acting) and breaks down the five building blocks: the LLM as the brain, tools as hands, short-term and long-term memory, self-correcting loops, and verification. Using a case study of a startup research agent for the fitness niche, the article walks through goal setting, tool integration, loop construction, memory implementation, and the addition of a critic agent, complete with copy-paste system prompts. It highlights six common failure modes and recommends a 2026 tech stack including Claude Code, LangGraph, and MCP. The piece provides a weekend roadmap to build a basic agent from a 50-line Python script and is aimed at developers shifting from prompt engineering to designing agent systems.

x.com · 21 min · Agent Architecture · AI Agents · AI Engineering