AI Agent Lifecycle
AI Agent Lifecycle defines the accountable lifecycle of agent work from intent to accepted outcome.
AI Agent Lifecycle is the discipline of keeping agent work continuous from human intent to accepted outcome. It treats intent, context, role, plan, confirmation, execution, evidence, learning, and closure as lifecycle state rather than loose prompt text or runtime exhaust. A lifecycle view asks whether a system can preserve the meaning of the work across sessions, tools, agents, reviews, and changes, not merely whether a single execution succeeded.
The problem it names
The problem it names is the gap between runnable agents and accountable work. An agent can produce a correct-looking output while losing the reason the work was requested, the constraints that made it acceptable, the authority boundary around the action, or the evidence needed for review. That failure is especially visible when work crosses multiple conversations, handoffs, agents, or tools. The project continues, but the agent system may only see the latest slice of text.
Why existing approaches are not enough
Prompt engineering can make one request clearer. Context engineering can improve what the model sees. Orchestration can make execution repeatable. Observability can record what happened. None of those alone defines what must remain valid before, during, and after execution. A lifecycle needs a grammar for continuity: what state is active, what has expired, what has been confirmed, which evidence is required, and when an outcome is accepted.
How it relates to AI Agent Lifecycle
AI Agent Lifecycle is the field definition for accountable agent work. Agentic Delivery names the category between execution and accountable outcomes. MPLP is the lifecycle protocol path, while Cognitive OS, SoloCrew, and Validation Lab continue the concrete proof path through runtime, delivery, and evidence adjudication.
White paper source trace
AI Agent Lifecycle is adjacent to GAIC through the field-definition layer; R3K-0 did not assign a direct chapter/table/MRO anchor for this route.
The page frames lifecycle continuity, while GAIC applies that continuity to MRO, RCCS-M, and ALCS.
The lifecycle lens asks what remains valid across intent, context, plan, confirmation, execution, evidence, review, and closure.
This source trace is author-analytical. It is not legal advice, certification, legal compliance proof, regulator approval, vendor ranking, procurement guidance, or a claim that MPLP is required.
Evidence route
The evidence route starts with the origin essay, because it explains why the category grew out of real engineering failure boundaries. From there, MPLP shows how lifecycle concepts can become protocol records, while Validation Lab keeps the delivery claim tied to inspectable evidence. The route is intentionally modest: it shows a thesis, a vocabulary, and proof path, not market adoption.