Building Effective LLMs Agents

Anthropic's article explores building effective AI agents using Large Language Models (LLMs), emphasizing simple and composable patterns over complex frameworks. It distinguishes between workflows (predefined code paths) and agents (dynamic, self-directed processes) and suggests starting with basic LLM APIs before adopting frameworks. The article presents common patterns for agentic systems, including prompt chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer, and autonomous agents, offering guidance on when to use each approach. It stresses the importance of simplicity, transparency, and well-designed agent-computer interfaces, particularly emphasizing thorough tool documentation and testing, to create reliable and maintainable AI agents. The article also highlights practical applications like customer support and coding agents, where these systems can be particularly valuable.

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