ChatSoftware agents usually struggle to adapt to new tasks without human intervention; a feedback-driven cycle now allows them to rewrite their own internal rulebooks and workflows while keeping their core intelligence fixed. This approach ensures agents grow more reliable and capable by learning from their past mistakes.
For decades, software assistants have relied on static instructions that limit their ability to handle shifting goals or complex environments. This rigidity creates a barrier where systems fail to improve their own logic, leaving them unable to evolve alongside the demands of the projects they support.
Now, a new framework treats the internal scaffolding of an agent like a living document that undergoes constant revision. By separating the parts of the code that propose changes from the mechanisms that test them, the agent iteratively refines its memory and workflows. Fresh validation data acts as a filter, ensuring that only beneficial adjustments remain in the system.
This process allows digital assistants to optimize their performance without altering their core foundations. By enhancing organizational workflows, by strengthening institutional accountability, and by improving physical safety, this method creates more dependable autonomous systems.
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 from revisions to tools and code, repository-level control logic, and plugin composition. We characterize search through evolutionary optimization and examine its reliability through continual PAC-learning. Under explicit assumptions, the analysis yields an expected generalization bound, a retention bound conditional on historical regression budgets, and a high-probability admission rule using fresh, independent validation data. We examine how persistent harness revisions affect knowledge sharing and organizational workflows, with implications for scientific reliability, physical safety, and institutional accountability. Cases from prior work illustrate model--harness co-design, distributed execution, and plugin reuse. Reliable deployment requires continual validation as task distributions and evaluators change, with independent safeguards for learning, runtime privileges, and consequential actions. We provide a comprehensive 66-page discussion of related work, theory, algorithms, evaluation, applications, and the broader impact of Harness RSI, together with continuously updated code and supplementary materials, at https://github.com/zgcagi/Harness-RSI.
Architectural floor planning for elder-care facilities required weeks of manual drafting to balance accessibility with regulatory safety codes; Conditional Generative Adversarial Networks (cGANs) autonomously generate optimized retirement home layouts.
