Self-Improving AI Agents: How Closed-Loop Systems Fix Broken Prompts Automatically

Self-Improving Skills for AI Agents: Building Systems That Learn from Failure Core Question: How can AI agent skills automatically adapt and improve when environments change, instead of relying on manual maintenance of static prompts that quickly become outdated? In AI agent development, we face a fundamental challenge: skills are typically static, but the environment around them constantly changes. A skill that worked perfectly a few weeks ago can silently begin failing when the codebase updates, when model behavior shifts, or when the types of tasks users request evolve over time. In most systems, these failures remain invisible until someone notices …