A Survey on Self-Improving Modern AI Agents
TheTuringPost · x · 2026-07-19
This is a comprehensive survey on the self-improvement of modern agents, detailing how they continuously improve through experience without requiring humans to manually patch every single step. The paper categorizes existing methods into two main approaches: - **Model Improvement**: Updating model parameters using generated examples, feedback, and experiential data. - **Scaffolding Improvement**: Upgrading prompts, memory, tools, controllers, and overall agent orchestration without altering model parameters. The survey also covers: - Sources of self-generated data, feedback, and experience - Tool creation and complete agent redesigns - Applications in domains like coding, science, and robotics - Evaluation, safety, and stability issues
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