Self-Evolving Agent Harness
Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model weights fixed. We formalize a feedback-driven process in which accepted revisions persist across runs. The framework separates update targets from proposal and admission mechanisms. It distinguishes changes to interpreted state fro
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Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model weights fixed. We formalize a feedback-driven process in which accepted revisions persist across runs. The framework separates update targets from proposal and admission mechanisms. It distinguishes changes to interpreted state fro
This research introduces a novel approach to making AI agents more robust and adaptive by allowing them to self-evolve their operational scaffolding without retraining the core model. For ML engineers, understanding this framework is crucial for building more resilient, autonomous, and cost-effective AI systems that can improve over time in production environments.