TL;DR AI agent lifecycle management is the practice of running every AI agent through seven controlled stages, from design to retirement. The stages are design, build, test, deploy, monitor, improve and retire. Each stage has a named owner, a required record and an exit test before the agent moves on. A central registry keeps one entry per agent, covering its
TL;DR Human in the loop AI agents pause before chosen actions so a person can approve, edit or reject them. Not every action needs that pause. Read-only and easily reversed steps can run alone, while payments, external messages and production changes wait for approval. Confidence scores and policy rules decide what else goes to a reviewer queue. Frameworks such as
TL;DR AI agent skills are folders of instructions, scripts and reference files that teach an AI agent how to do one specific task well. Each skill centers on a SKILL.md file that holds a name, a description and step-by-step guidance. An agent reads only the short descriptions at first and loads the full instructions when a task matches. Skills sit
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