Build-time Governance
Lifecycle phases define business value, architecture, agent roles, data foundations, engineering quality, release controls, observability, and KPI measurement.
Enterprise AI lifecycle gap
Delivery lifecycles build AI. Runtime governance controls AI Workers.
Enterprise AI lifecycle models help teams move from strategy to architecture, agent design, engineering, deployment, operations, and business outcomes. Production agentic systems need a second control plane that decides whether each AI Worker action is identified, scoped, authorized, evidenced, reviewable, and revocable at runtime.
Public preview notice: This resource is educational and planning-oriented. It does not approve production use or authorize AI Workers, does not provide certification, does not validate security posture, does not provide legal advice, does not create compliance determinations, does not create an audit opinion, and does not deploy live runtime enforcement.
The Structural Gap
Action-time Governance
Runtime controls decide whether a specific AI Worker may use a tool, API, connector, data class, memory layer, model, workflow, or destination for a requested action.
Evidence-time Governance
Review-grade governance reconstructs who owned the worker, what authority applied, what decision occurred, what evidence was recorded, and what lifecycle state followed.
Lifecycle-to-Runtime Mapping
Lifecycle phase
What the phase establishes
Runtime governance still required
Strategy and value discovery
Business case, outcomes, prioritized use cases, stakeholders, and KPI intent.
Named AI Worker owner, sponsor, risk tier, approved purpose, lifecycle status, and review accountability in the Passport.
Enterprise and solution architecture
Capability maps, data architecture, integration architecture, security principles, and deployment posture.
Tool/API/Connector Registry scope for approved actions, data classes, systems, destinations, prohibited actions, and trust boundaries.
Agent strategy and design
Agent roles, tasks, personas, guardrails, orchestration pattern, and human-in-the-loop expectations.
Toll Gate Decisions and Human Review rules that separate recommendation, approval, escalation, exception, and blocked states.
Data and context foundation
Data sources, context retrieval, knowledge graphs, vector stores, metadata, and data quality controls.
Runtime checks for data classification, source authority, memory/RAG boundaries, retention posture, redaction, and evidence references.
Engineering, build, deployment, and release
Model selection, application code, APIs, testing, infrastructure, CI/CD, change windows, rollback, and release evidence.
Runtime Permits for short-lived scoped authority and Action Broker mediation before executable or consequential actions proceed.
Operations, runtime intelligence, and outcomes
Monitoring, observability, incident management, AIOps, cost optimization, value tracking, and improvement loops.
Evidence Records, Stamps, Workflow Events, Agent Action Decisions, Audit Export Packages, lifecycle analytics, pause, suspension, revocation, and retirement handling.
Runtime Governance Layer
Identity and Scope
The Agent Registry and Passport identify the AI Worker, human owner, approved purpose, risk tier, lifecycle state, permissions, prohibited actions, and evidence posture.
Policy and Review
Toll Gates and Human Review items determine whether a requested action is allowed, denied, blocked, escalated, or held until additional evidence is provided.
Permit and Broker
Runtime Permits express scoped, short-lived authority for a specific action. The Action Broker mediates controlled execution rather than allowing unmanaged tool use.
Evidence and Reconstruction
Evidence Records, Stamps, Workflow Events, Agent Action Decisions, and Audit Export Packages preserve the decision trail needed for review, monitoring, and follow-up.
Governance checkpoints that should trigger before action proceeds
Deployment approval is not runtime authorization; each consequential action still needs scoped authority, evidence, and an accountable review path.
- Passport missing, expired, paused, suspended, revoked, retired, or outside approved purpose.
- Tool, API, connector, data class, memory layer, model, workflow, or destination outside approved scope.
- Human Review required because the action is consequential, externally exposed, sensitive, high-risk, or ambiguous.
- Required evidence, source reference, rollback path, owner, reviewer, or escalation route is missing.
- Cross-organization trust boundary, external communication, data movement, or agent-to-agent delegation is requested.
Hard Tradeoffs
Speed vs. Authority
Low-friction agent deployment is useful, but enterprise control requires scoped runtime authority. A released agent should still fail closed when permit, scope, owner, or evidence is missing.
Observability vs. Auditability
Logs and metrics show behavior. Audit-grade evidence must also preserve decisions, policy versions, review outcomes, related records, lifecycle state, and reconstruction context.
Automation vs. Accountability
AI Workers may draft, classify, summarize, recommend, route, and prepare evidence. Humans remain accountable for approval, scope expansion, exceptions, and production decisions.