Agent ready episode 6 with Honeycomb: observability & SLOs with AI agent workloads

In the sixth episode of the "Agent Ready" video series, Jessica Kerr from Honeycomb discusses the importance of observability and service level objectives (SLOs) in understanding AI agent workloads, particularly within systems driven by large language models (LLMs). The session highlights the challenges of non-deterministic AI systems and the necessity of tracing and instrumentation to gain insights into their behavior. Jessica explains how distributed tracing can provide a detailed picture of software performance, especially when AI components are involved, allowing developers to better understand user interactions and system outputs. The discussion also covers the use of open telemetry for effective instrumentation and the integration of AI evaluation techniques to assess the accuracy and performance of AI-driven features. Jessica emphasizes the importance of observability in making informed decisions to improve production behavior, optimize costs, and enhance user experience, while also providing insights into the integration of AI tools like Honeycomb's Query Assistant, which uses LLMs to assist developers. This session is part of a broader effort to address the evolving landscape of AI applications by ensuring they are both observable and efficient.

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