Building reliable LLM agents in production requires more than just calling an API. Three key patterns have emerged: ReAct (Reasoning + Acting) which interleaves reasoning traces with actions, Chain-of-Thought (CoT) which decomposes complex problems into step-by-step logic, and self-reflection which enables agents to critique and correct their own outputs. This post provides a practical engineering guide to implementing these patterns, discussing trade-offs in latency, cost, and accuracy. For example, ReAct is effective for tool-use scenarios but can be verbose, while CoT improves reasoning quality at the cost of longer prompts. Self-reflection adds a feedback loop that catches errors but increases complexity. The author shares concrete code examples and deployment considerations, making this a valuable resource for teams building autonomous agents. The combination of these techniques is becoming a standard architecture for advanced AI systems, from coding assistants to customer support bots.
This post explores three critical patterns for production LLM agents: ReAct for reasoning-action loops, Chain-of-Thought for structured thinking, and self-reflection for error correction. It provides practical insights on how to combine these techniques to build more robust and autonomous AI systems, a topic of high interest to engineers deploying agents at scale.