autogen/website/docs/topics/llm-observability.md
What is Observability? Observability provides developers with the necessary insights to understand and improve the internal workings of their agents. Observability is necessary for maintaining reliability, tracking costs, and ensuring AI safety.  **Without observability tools, developers face significant hurdles:**  - Tracking agent activities across sessions becomes a complex, error-prone task. - Manually sifting through verbose terminal outputs to understand LLM interactions is inefficient. - Pinpointing the exact moments of tool invocations is often like finding a needle in a haystack.   **Key Features of Observability Dashboards:** - Human-readable overview analytics and replays of agent activities. - LLM cost, prompt, completion, timestamp, and metadata tracking for performance monitoring. - Tool invocation, events, and agent-to-agent interactions for workflow monitoring. - Error flagging and notifications for faster debugging. - Access to a wealth of data for developers using supported agent frameworks, such as environments, SDK versions, and more.  ### Compliance  Observability is not just a development convenience—it's a compliance necessity, especially in regulated industries: - It offers insights into AI decision-making processes, fostering trust and transparency. - Anomalies and unintended behaviors are detected promptly, reducing various risks. - Ensuring adherence to data privacy regulations, thereby safeguarding sensitive information. - Compliance violations are quickly identified and addressed, enhancing incident management. 