Yusuf Ishola's guide, "Building and Monitoring AI Agents with Helicone: A Step-by-Step Guide (Part 2)," addresses the issues identified in a financial research assistant developed in Part 1, which had problems with knowledge base queries and inappropriate tool usage. The guide introduces Helicone's comprehensive observability features to improve the agent's performance by implementing monitoring through Helicone Sessions, which provides visibility into the agent's workflow and decision-making process. Key fixes include adjusting the RAG threshold from 0.9 to 0.7 to improve retrieval success, enhancing tool selection logic with explicit guidance to minimize unnecessary tool usage, and upgrading the model from GPT-3.5 Turbo to GPT-4o Mini for better reasoning capabilities. The guide emphasizes the importance of comprehensive monitoring to uncover hidden performance issues, such as rate limiting, and outlines strategies to optimize agent performance and reliability, concluding that effective observability tools are crucial for building production-ready AI agents.