When agents improve agents

The third and final installment of a series discusses the evolution of a self-improving loop that not only operates independently but also enhances its performance over time, emphasizing the importance of a reliable judge to evaluate the outcomes. The loop differs from conventional systems by continuing its processes until achieving its goals rather than stopping when the plan is exhausted. It incorporates a control plane that monitors external changes, such as CI updates or review threads, to initiate new runs. The loop's advancement lies in internalizing its evaluation process, making decisions on whether to continue based on the achievement of set goals, verification, and feedback. The system also utilizes memory and history search capabilities to learn from past runs, enabling it to avoid repeating mistakes. However, the evaluation process is complicated by the potential biases of the judging models, which require careful calibration against human expertise to ensure accuracy. The series concludes that while the technology isn't yet fully autonomous, the foundational components for a self-improving loop are in place, allowing for incremental improvements and adjustments in real-time traffic through tools like Pydantic Logfire.

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