Building AI Agents in Kotlin – Part 3: Under Observation | The JetBrains AI Blog

In the third installment of a series on building AI agents in Kotlin, the article addresses the growing complexity of debugging as AI agents become more capable, highlighting the need for enhanced observability to improve, debug, and estimate costs effectively. The article discusses integrating tracing with Langfuse, an open-source observability tool, to provide a detailed view of the agent's actions, including cost per action. This integration reveals the agent's decision-making trajectory, helping to identify inefficiencies and errors that are difficult to detect through traditional logging methods. By implementing Langfuse, the development team can track important metrics like token usage and costs, providing insights into the agent's performance. This visibility aids in identifying specific issues, such as a tool's failure to handle line count discrepancies, and facilitates more informed decisions about task delegation in future developments. The article emphasizes the importance of observability not only for debugging but also for gaining a deeper understanding of the agent's behavior, ultimately leading to more efficient and effective AI agents.