Concepts Map / Entity Mesh

Author identity and site entity anchor.

The citation and evidence shelf for owned-canonical, authored-analysis, project, mapping, and pending-external-evidence surfaces.

Machine-readable graph artifact for the Jearon Wong / MPLP / GAIC entity system.

Public role anchor for the AI agent lifecycle body of work.

The accountable lifecycle of agent work from intent to accepted outcome.

The missing layer between agent execution and accountable outcomes.

The governance model for lifecycle responsibility in AI agent and multi-agent work.

The public research authority source for MRO, RCCS-M, ALCS, and lifecycle responsibility governance.

The public research edition for agentic AI auditability, lifecycle evidence, audit evidence chains, AARM, and MRO-to-audit-evidence mapping.

The public research edition has HTML/PDF artifacts, manifest, and checksum; the withdrawn v0.2 candidate is not current source truth.

Auditability framing for reconstructing agentic work through lifecycle evidence.

Object model for lifecycle-linked auditability of agent work.

Responsibility-linked evidence chain that distinguishes raw logs from reviewable audit evidence.

Agentic Auditability Readiness Model for L0-L5 auditability readiness.

Public research edition framing for agentic work through lifecycle evidence, responsibility mapping, bounded risk objects, and claim-reviewable records; v0.2 body withdrawn and not current source truth.

AIO is a Jearon Wong analytical object model for separating insured legal subject, agentic risk object, responsibility, evidence, loss reconstruction, dependency, aggregation, and dispute-readiness questions.

Public research edition concept for reconstructing authority, action, loss event, dependency, remediation, dispute posture, and boundary risk for claim review.

AIRM is a Jearon Wong non-scoring reasoning model for evidence visibility and risk-transfer reviewability.

Source-qualified, non-ranking lifecycle governance mappings for systems discussed in the white paper.

Source-qualified lifecycle governance mappings for ecosystems outside the first-layer GAIC-cited system set.

Source-qualified lifecycle governance mappings for regulatory, enterprise, privacy, risk, management-system, evidence, substitution, and incident search intents.

Lifecycle responsibility compliance mapping through MRO, RCCS-M, ALCS, evidence, acceptance, and legal-review boundaries.

Enterprise accountability, evidence retention, substitution, accepted outcome, auditability, and incident closure mapping.

Cautious mapping between EU AI Act themes and agentic lifecycle objects. It is not legal advice or compliance proof.

Privacy and evidence-chain mapping for minimization, data subject rights, processor chains, retention boundaries, and validation.

Non-official mapping from NIST AI RMF Govern, Map, Measure, and Manage to lifecycle governance concepts.

Cautious mapping between AI management system language and agentic lifecycle responsibility objects.

Evidence chains, logs, minimization, retention boundaries, privacy tension, replay, dispute, remediation, and evidence partitioning.

Model, runtime, tool, vendor, prompt, and harness substitution mapped to authority, evidence, and accepted outcome continuity.

Incident, dispute, rollback, accepted outcome reversal, remediation, owner responsibility, and lifecycle closure records.

Lifecycle responsibility objects missing from model-centric governance.

The object layer behind Agentic Lifecycle Governance.

MRO-adjusted Regulatory Compliance Coverage Score.

Agentic Lifecycle Conformance Score.

One protocol path for expressing lifecycle responsibility semantics.

Defines who may authorize agentic action and under what scope.

The point where agent work becomes reviewable, attributable, and accepted responsibility.

Structured proof for review, replay, dispute, remediation, and acceptance.

The point where autonomous execution becomes authorized responsibility.

Making agentic work scoped, configured, authorized, evidenced, reviewed, accepted, remediable, and rollbackable without claiming deterministic model output.

Agent workflows that can return to known lifecycle state with authority, evidence, accepted outcome, and remediation records intact.

Agent systems whose lifecycle state can be inspected, replayed, disputed, reviewed, accepted, and closed.

Governance profiles, authority limits, tool constraints, context boundaries, and substitution rules that remain evidence-linked.

Responsibility architecture for human roles, agent roles, tools, evidence, accepted outcome, rollback, and remediation.

Wrapping agent execution with lifecycle boundaries, evidence capture, rollback, remediation, and acceptance.

A playbook explaining why prompts express intent while harnesses govern execution boundaries, evidence, rollback, and accepted outcome.

A practical checklist for architecting agent workflows around intent, context, authority, tools, evidence, accepted outcome, rollback, substitution, and human responsibility.

Practical guides for rollback, verification, auditability, vendor workflows, and human-role mapping.