Start with one question.
AI Agent Lifecycle defines the accountable lifecycle of agent work from intent to accepted outcome.
- 01Thesis
- 02Field definition
- 03Dynamic agent reality
- 04Governance primitives
- 05Proof path
- DEFINITION
- AI Agent Lifecycle defines the accountable lifecycle of agent work from intent to accepted outcome.
- BOUNDARY
- Author-defined category framing; not a certification, legal-compliance, regulator, or procurement standard.
- EVIDENCE
- Lifecycle source boundary links the GAIC white paper, Concept Core, definitions, and the MPLP project path.
- CITATION
- Cite the AI Agent Lifecycle field page together with the GAIC white paper when discussing governance objects.
- SOURCE_LEVEL
- AUTHORED_FIELD_DEFINITION
- UPDATED
- 2026-08-26
The lifecycle is the unit of accountability.
The field moves from intent through activation, process, and project state to an accepted outcome with responsibility and evidence still attached.
- 01IntentWhat is being asked
- 02ActivationContext and authority
- 03ProcessPlan, execute, verify
- 04ProjectState and responsibility
- 05Accepted outcomeReview, evidence, recovery
Execution is not Delivery.
Execution proves that an agent can act. Delivery proves that the work reached an accepted outcome with responsibility, evidence, and authority still attached.
The field definition
AI Agent Lifecycle defines the accountable lifecycle of agent work from intent to accepted outcome.
It is not merely:
Those systems may be useful. They still do not define the accountable lifecycle of agent work.
Category definition, not a compliance framework.
This page defines the AI Agent Lifecycle category and the accountable-delivery thesis. The GAIC white paper and Agentic Lifecycle Governance concept route provide the source-traced governance layer for Missing Regulatory Objects, RCCS-M, ALCS, and lifecycle responsibility compliance. This page is not legal advice, certification, regulator approval, procurement guidance, or a claim that one implementation is required.
The dynamic agent reality
Static definitions cannot govern dynamic agents.
If agents are dynamic, governance cannot be static.
The market built static governance for dynamic agents. That is the foundational failure.
The dominant market routes, from AWS, Google, Microsoft, Salesforce, IBM, and LangChain, often begin by defining agents, assigning tools, setting policies, routing workflows, and observing execution.
These systems may be useful. They still do not define dynamic lifecycle governance for accountable agent work.
MPLP specifies lifecycle semantics for governing agent work as a dynamic lifecycle.
This page names public market routes as category references. The source-backed breakdown belongs in the Agentic Delivery essay series.
The category
Agentic Delivery names the missing layer between agent execution and accountable outcomes.
Agentic Delivery is the category.
MPLP is the lifecycle protocol path within that category.
Governance scope
Multi-Agent Lifecycle Governance is the multi-agent form of Agentic Delivery: governing responsibility, authorization, evidence, and outcome acceptance across agents, roles, projects, and lifecycle stages.
Multi-agent is not headcount. It is responsibility architecture.
Governance primitives
Confirmation Boundary
The lifecycle point where autonomous execution becomes authorized responsibility.
Evidence Chain
Structured proof that agent work can be reviewed, replayed, disputed, and accepted.
Semantic Loss
The degradation of intent, constraints, responsibility, and evidence across lifecycle handoffs.
Semantic Loss is the failure mode.
Evidence Chain is the governance response.
Protocol path
MPLP is the lifecycle protocol path for making Agentic Delivery explicit, governable, and auditable.
MPLP does not equal Agentic Delivery. It is the protocol path inside the category.
Runtime substrate note
PSG / AEL / VSL are reserved runtime substrates for future technical deep-dives.
Forthcoming technical depth. Not the first public surface.
Proof path
Cognitive OS → SoloCrew → Validation Lab.
Cognitive OS
Runtime path for protocol-native agent work.
Cognitive OS is a protocol-native runtime path for state, activation, projection, constraints, and evidence capture.
SoloCrew
Delivery proof path for one-person-company AI operations.
SoloCrew is a delivery proof path for applying Agentic Delivery to one-person company operations.
Validation Lab
MPLP evidence adjudication surface.
Validation Lab is an MPLP evidence adjudication surface for evaluating evidence packs under versioned rulesets.
Execution is not Delivery. The AI Agent Lifecycle is the field where accountable agent work begins.