Prompting for Agents | Code w/ Claude

Hannah and Jeremy from Anthropic's Applied AI team discuss prompting strategies for AI agents, contrasting them with basic prompting techniques. They define agents as models using tools in a loop for complex tasks, emphasizing the importance of understanding the agent's environment and providing clear heuristics. They advise against using agents for simple tasks and highlight the need to consider the cost of errors. Jeremy shares best practices for prompting, including thinking like the agent, guiding the thinking process, and managing the context window, and the importance of tool selection. They also touch on evaluation methods for agents, such as answer accuracy, tool use accuracy, and TauBench, and finally answer questions from the audience on prompt building.