Start with one question.
The public arguments behind AI Agent Lifecycle, Agentic Delivery, lifecycle governance, and the protocol path.
- 01Featured column
- 02Lifecycle sequence
- 03Foundation essays
- 04Protocol and governance
- 05Research commentary
Read the essays as an argument sequence.
The ledger moves from the lifecycle problem to protocol, governance, and evidence instead of presenting every essay as an isolated post.
- 01IntentWhat is being asked
- 02ActivationContext and authority
- 03ProcessPlan, execute, verify
- 04ProjectState and responsibility
- 05Accepted outcomeReview, evidence, recovery
The AI Agent Lifecycle
Execution is not Delivery. These essays develop the argument. Use /lifecycle/ for the theory entry; use this page for the writing order.
Define The AI Agent Lifecycle
When an Agent Says "Done," Who Accepts the Outcome?
An Agent can complete its execution while the work remains unaccepted. A first-person field essay from building MPLP and its Development Runtime about Intent change, evidence, authority, and the lifecycle decision that turns execution into delivery.
The Industry Is Still Debating AI Agent Governance. MPLP Already Defines the Lifecycle Answer.
The industry keeps debating AI agent governance as fragmentation, audit gaps, runtime drift, weak HITL accountability, and uninsurable agentic risk. MPLP defines the missing lifecycle governance layer behind them.
Defining Intent Drift in Agentic Workflows
Agentic systems do not only fail when models hallucinate. They fail when intent degrades across human input, model interpretation, and long-running execution. This essay defines Intent Drift, Delta Intent, and Drift Detection as the minimum defense.
From Model Governance to Agentic Lifecycle Conformance
Jearon Wong explains how studying EU AI Act, GDPR, NIST AI RMF, ISO/IEC 42001, Singapore governance work, W3C provenance standards, and Colorado AI Act led from traditional compliance coverage to Missing Regulatory Objects, RCCS-M, and ALCS.
Agent Orchestration Is Not Delivery
The third essay in the Define The AI Agent Lifecycle series. Jearon Wong argues that agent orchestration coordinates execution but does not define delivery: in the AI Agent Lifecycle, the orchestrator must become the consensus layer that turns human intent into verifiable responsibility and accepted project delivery.
The Industry Misdefined Multi-Agent AI
The second essay in Define The AI Agent Lifecycle. Jearon Wong argues that MAS should be defined by lifecycle responsibility separation, lifecycle-governed workflows, Agent Lifecycle Protocol, and accountable delivery.
AI Agent Lifecycle: It Was Not Designed. It Grew.
The first essay in Define The AI Agent Lifecycle. A personal engineering origin story of how prompt failure, context drift, intent drift, and multi-agent coordination exposed the need for AI Agent Lifecycle.
Foundation Essays
The Agentic AI Inflection Point: Project Delivery
Agentic AI is moving from task execution to project delivery, where real work must be carried from human intent to accepted outcome.
Protocol / Governance Essays
Governed by Design: The Protocol-Native Multi-Agent Operating System for the Next Era of AI
Agentic AI does not need another framework or dashboard. It needs a protocol-native lifecycle layer for intent, drift, confirmation boundaries, evidence chains, agent handoffs, and accepted outcomes across system boundaries.
MCP Connects Tools. A2A Connects Agents. Who Governs the Lifecycle?
Production AI agents need a vendor-neutral lifecycle governance layer above tool access and agent coordination.
Auditability / Assurance Research
From Static Logs to Dynamic Evidence Chains: The Auditability Era of Agentic AI
Jearon Wong argues that Agentic AI auditability requires a shift from static logs to dynamic, responsibility-linked lifecycle evidence chains.