FIELD ANALYSIS / AI AGENT LIFECYCLE

Execution is not Delivery.

AI agents will not become real infrastructure until execution becomes accountable delivery.

AI Agent Lifecycle examines how agent work moves from intent to accepted outcome.

Jearon Wong Protocol Architect for the Agent Era
Open the field analysis Inspect the Proof Path
Jearon Wong portrait
RESEARCH SERIES: AGENTIC LIFECYCLE GOVERNANCE INDUSTRY SERIES

Whitepaper series.

The first three public research editions establish the compliance, auditability, and insurability foundation for agentic lifecycle evidence. MPLP v2.0 object-model consolidation is the next protocol phase; the enterprise implementation white paper and practitioner guides are held until that object model is ready.

  1. 01 Foundation
    FOUNDATION: GLOBAL COMPLIANCE

    Global AI Compliance White Paper 2026

    Defines Missing Regulatory Objects, RCCS-M, ALCS, and lifecycle responsibility governance for AI agent and multi-agent systems.

    v0.3.2 Public Research Edition485-page PDF
    Read paper Download PDF
  2. 02 Auditability & Assurance
    AUDITABILITY & ASSURANCE: EVIDENCE SPECIALIZATION

    Agentic AI Auditability & Assurance White Paper 2026

    Specializes the series into audit evidence chains, AARM, and MRO-to-audit-evidence mapping for enterprise AI governance.

    v0.1 Public Research Edition118-page PDF
    Read paper Download PDF
  3. 03 Insurability & Risk Transfer
    INSURABILITY & RISK TRANSFER: RISK-TRANSFER EVIDENCE

    Agentic AI Insurability & Risk Transfer White Paper 2026

    A public research edition analyzing agentic AI insurability and risk transfer through lifecycle evidence, insured subject separation, and claim reconstruction boundaries.

    v1.0 Public Research Edition138-page PDFNo public DOCX
    Read paper Download PDF
View Evidence Registry Explore Concept Core
SYSTEM_MAP: SECONDARY_REFERENCE_ARCHITECTURE

Agentic Delivery Stack

Agentic Delivery names the missing layer between agent execution and accountable outcomes.

The Agentic Delivery Stack is a secondary reference architecture for turning agent execution into scoped, authorized, traceable, reviewable, and accepted outcomes.

Secondary reference architecture. MPLP is the protocol path.

CORE THESIS

Delivery, not isolated execution

AI agents do not become infrastructure because they can act. They become infrastructure when their work can be scoped, authorized, traced, reviewed, and accepted.

LIFECYCLE PROTOCOL Protocol

Lifecycle Protocol

Shared lifecycle semantics for context, plan, confirmation, responsibility boundary, and trace.

EXECUTION RUNTIME Runtime

Execution Runtime

State, activation, constraints, projection, and runtime evidence capture.

DELIVERY SURFACE Delivery

Delivery Surface

The surface where agent activity becomes user-facing or organization-facing deliverables.

EVIDENCE / ADJUDICATION Adjudication

Evidence / Adjudication Surface

Evidence packs, review, challenge, comparison, and ruleset-based adjudication.

CORE_THESIS

Execution is not Delivery.

Most agent systems optimize execution: prompts, tool calls, workflow runs, traces, and evaluations.

My work starts from delivery: AI agents will not become real infrastructure until execution becomes accountable delivery.

Task Agent
Prompt Tool call Output
Agentic Delivery
Intent Context Plan Confirm
Execute Evidence Accepted Outcome
CATEGORY_ENTRY: AI AGENT LIFECYCLE

Start from AI Agent Lifecycle.

AI Agent Lifecycle is the field-definition layer. Agentic Delivery names the category. MPLP is the protocol path within that category.

Prompt Engineering improves a response. Context Engineering improves what the model sees. Harness Engineering improves execution. AI Agent Lifecycle asks what must stay dynamic, governable, and accountable after execution.

Open the field definition Read the Essays Inspect the Proof Path

Protocol Path and Proof Path

Cognitive OS, SoloCrew, and Validation Lab form the concrete proof path through Agentic Delivery. MPLP is the protocol path that makes it governable and auditable.

Inspect the Proof Path
PROJECT_ID: COGNITIVE OS
PATH_ROLE: RUNTIME PATH
STATUS: RUNTIME PATH

Cognitive OS

Runtime path for protocol-native agent work.

RECORD_CLAIM

Cognitive OS is a protocol-native runtime path for state, activation, projection, constraints, and evidence capture.

Open project record
EVIDENCE_SURFACE
SOURCE Cognitive_OS
PROJECT_ID: SOLOCREW
PATH_ROLE: DELIVERY PROOF PATH
STATUS: DELIVERY PROOF PATH

SoloCrew

Delivery proof path for one-person-company AI operations.

RECORD_CLAIM

SoloCrew is a delivery proof path for applying Agentic Delivery to one-person company operations.

Open project record
EVIDENCE_SURFACE
SOURCE SoloCrew
PROJECT_ID: VALIDATION LAB
PATH_ROLE: EVIDENCE ADJUDICATION
STATUS: EVIDENCE ADJUDICATION

Validation Lab

MPLP evidence adjudication surface.

RECORD_CLAIM

Validation Lab is an MPLP evidence adjudication surface for evaluating evidence packs under versioned rulesets.

Open project record
EVIDENCE_SURFACE
SOURCE MPLP-Validation-Lab SITE lab.mplp.io

Reading Path

The essays develop the public argument after the Lifecycle mainline is clear.

Read the Essays

Writing tracks.

Supporting lines of inquiry across protocols, governance, runtime, delivery, and product.

Protocol Engineering

Lifecycle vocabulary, handoff semantics, and delivery grammar for agent systems.

Reliable Agent Delivery

How agent systems complete real work — plannable, verifiable, and auditable.

Agent Governance & Evidence

Oversight structures, compliance surfaces, and accountability frameworks for autonomous work.

AI Programming & Delivery Systems

Runtime architecture, state management, and operating constraints for protocol-aware agents.

Agent Stack Commentary

Analysis and perspective on the evolving agent infrastructure landscape.

Get in touch.

For industry dialogue, advisory, collaboration, or media.

contact@jearonwong.com