
Developed token-level observability for large language model interactions within the All-Hands-AI/agent-sdk repository, focusing on backend development and event-driven programming using Python. Designed and implemented the TokenEvent data model and processing logic to capture token IDs for both prompts and responses, enabling detailed tracking and analysis of LLM interactions. This work laid the foundation for reinforcement learning experiments by facilitating data-driven optimization and richer analytics, supporting future improvements in cost, latency, and performance. Consolidated token-event handling within the SDK to streamline analytics and tooling, enhancing the ability to monitor and optimize machine learning workflows at a granular level.
November 2025: Delivered TokenEvent tracking for LLM interactions in the All-Hands-AI/agent-sdk, enabling token-level observability and better RL analysis. Implemented TokenEvent data model and processing to capture token IDs for prompts and responses, facilitating data-driven optimization and reinforcement learning workflows.
November 2025: Delivered TokenEvent tracking for LLM interactions in the All-Hands-AI/agent-sdk, enabling token-level observability and better RL analysis. Implemented TokenEvent data model and processing to capture token IDs for prompts and responses, facilitating data-driven optimization and reinforcement learning workflows.

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