Developing effective prompts for AI agents is an iterative process. Initial designs frequently need adjustment to ensure agents perform as intended and achieve reliable outcomes in their workflows. This chapter focuses on the practical aspects of refining these prompts.

You will learn to:

• Identify common difficulties that arise when implementing prompts for agentic systems.
• Apply a systematic approach to iterating on and testing your prompt designs.
• Understand how sequences of prompts, or prompt chaining, affect agent outputs.
• Employ methods for analyzing the action sequences an agent takes.
• Compare different prompt variations to determine which are most effective for specific tasks.
• Use logging and monitoring to gather data for ongoing prompt improvement.
• Establish practices for organizing and versioning your agent prompts.

The chapter culminates in a hands-on exercise where you will apply these techniques to debug and optimize prompts within a sample agentic workflow, enhancing its performance and reliability.