Anthropic has published detailed guidance on developing effective AI agents with large language models (LLMs), drawing from their experience working with numerous teams across industries. According to authors Erik Schluntz and Barry Zhang, the most successful implementations rely on simple, composable patterns rather than complex frameworks.
The company distinguishes between two types of agentic systems: workflows, which follow predefined code paths, and agents that independently direct their processes and tool usage. The article emphasizes that developers should start with the simplest possible solution before adding complexity, as agentic systems often trade latency and cost for improved task performance.
The guidance outlines several fundamental patterns, including prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer workflows. Each pattern serves specific use cases, from handling customer service queries to completing complex coding tasks.
While acknowledging the utility of frameworks like LangGraph and Amazon Bedrock’s AI Agent framework, Anthropic recommends that developers begin by working directly with LLM APIs to better understand the underlying mechanisms. The company also stresses the importance of careful tool design and documentation for successful agent implementation.
Customer support and software development emerge as particularly promising applications for AI agents, with several companies already demonstrating success through usage-based pricing models. The article concludes by emphasizing that effective LLM implementation isn’t about building the most sophisticated system, but rather creating the right solution for specific needs.