The AI Control Plane
Source-grounded pack with Run an Agent Incident Retrospective, Map the AI System Registry, and Define AI Control-Plane Operating Policy, plus 1 more.
Get this packGrounded in the complete The AI Control Plane series.
Open the pack, choose a workflow, and follow its setup and input instructions. You can also open each workflow below.
10 source essays · 4 usable skills · reviewed Jul 2026
Use this pack when
Choose this pack when the work touches The AI Control Plane and you need a bounded workflow with explicit inputs, a defined output, and human review gates.
Choose the job in front of you.
4 source-grounded skills. Load one workflow at a time.
Run an Agent Incident Retrospective
Use this workflow when reconstructing an AI control-plane failure or near miss across identity, permissions, context, memory, model routing, budgets, evals, tools, approvals, actions, detection, rollback, and policy improvements.
Output specification
Return an agent_incident_retrospective with impact, scope, containment, timeline, identity_chain, context_and_memory, routing_and_evals, tool_actions, spend, human_controls, detection, failed_controls, root_assumptions, state_repair, system_changes, and validation.
Map the AI System Registry
Use this workflow when inventorying live AI copilots, agents, automations, product features, scripts, and vendor tools with accountable identity, purpose, owners, users, data, models, tools, spend, quality gates, review paths, and lifecycle status.
Output specification
Return an ai_system_registry with systems, registry_id, purpose, workflow, status, owners, invokers, runtime_identity, data, memory, models, tools, actions, permissions, spend, evals, logs, review, risk, expiry, and findings.
Define AI Control-Plane Operating Policy
Use this workflow when defining runtime policy for AI model routing, budgets, context and memory, evaluation release gates, observability, human review, escalation, incidents, and lifecycle governance across registered workflows.
Output specification
Return a control_plane_policy with policy_inputs, routing, budgets, memory_governance, release_gates, observability, review_rules, escalation_rules, incident_controls, lifecycle, owners, and scale_test.
Review AI Permission and Action Boundaries
Use this workflow when reviewing an AI agent or workflow's human, agent, service, data, memory, tool, and action permissions with least privilege, graduated autonomy, volume limits, approval, revocation, and runtime evidence.
Output specification
Return a permission_boundary_review with identity_chain, delegation, data_scopes, memory_scopes, tool_verbs, object_scopes, action_modes, limits, approvals, revocation, audit_evidence, findings, and required_changes.
Keep consequential decisions explicit.
These workflows structure analysis and artifacts; they do not authorize autonomous external, personnel, financial, legal, security, or other high-impact action.