AI Agent Governance And Limits
Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.
Use When
- Set step, wallclock, token, model, and cost budgets for agents.
- Define reversibility, blast-radius limits, and safe failure behavior.
- Connect runtime controls to policy, tenant limits, and operational review.
Do Not Use When
- The work is not AI-specific or agentic-AI-specific.
- A narrower retained AI parent skill fits the request better.
Required Inputs
- Product, tenant, user, data, risk, and operational context relevant to the AI workflow.
- Target artifact: design, implementation plan, audit, test strategy, UX flow, commercial policy, or runbook.
- Constraints from security, privacy, reliability, billing, support, and compliance stakeholders when relevant.
Workflow
- Read this SKILL.md first.
- Load references/routing.md to select the absorbed child reference that matches the task.
- Load only the selected child reference files needed for the current request.
- Produce execution-oriented output with assumptions, risks, evidence, and next actions where relevant.
Quality Standards
- Keep routing explicit: name which reference files were used when the work depends on absorbed material.
- Preserve tenant isolation, auditability, cost controls, safety gates, and operational evidence when they matter.
- Prefer concrete contracts, checklists, tables, schemas, runbooks, and decision records over broad summaries.
Anti-Patterns
- Loading every absorbed reference by default.
- Treating AI-specific billing, compliance, safety, or UX concerns as generic SaaS work without checking AI failure modes.
- Hiding retired skill names; old slugs must remain discoverable through references/routing.md.
Outputs
- A concrete deliverable matched to the request: architecture, implementation plan, audit, policy, runbook, UX flow, test strategy, or operating model.
- The selected consolidated reference files and any assumptions, risks, evidence requirements, or follow-up actions that affect execution.
References
- references/routing.md maps retired child skill slugs to their consolidated reference folders.
- Load references/agent-governance-and-oversight.md when deciding code-versus-model split, deferral to humans, safeguard components, agent registry, readiness, or NIST AI RMF evidence mapping.
Consolidated Child References
- Load references/routing.md to map retired AI child skill slugs to their reference modules.
Evidence Produced
| Category | Artifact | Format | Example |
|---|---|---|---|
| Security | Agent limits and reversibility register | Markdown table | step budget, blast radius, approval tier, kill path, and drill result |
Inputs
| Artefact | Required? | Purpose |
|---|---|---|
| Agent task and tool catalogue | yes | Identify actions and costs |
| Step, token, time, money, and blast-radius policy | yes | Define limits |
| Escalation and termination policy | yes | Stop unsafe runs |
Capability contract
Read and search are required. Budget enforcement tests may execute only in controlled environments. Changing production limits or kill switches requires explicit operational authority.
Degraded mode
Fallback without telemetry or enforcement access: produce the limit policy and gap register; do not claim limits are enforced.
Decision rules
| Limit event | Runtime response | Failure avoided |
|---|---|---|
| Soft threshold reached | Warn, checkpoint, or degrade | Abrupt failure |
| Hard cost/step/time threshold reached | Stop safely and preserve state | Runaway execution |
| Blast-radius or policy boundary reached | Deny and escalate | Unsafe autonomy |
Domain anti-patterns
- Limits documented only in prompts. Fix: enforce outside the model.
- One global budget for every tenant and task. Fix: scope by risk and entitlement.
- Retrying after a hard limit. Fix: require continuation authority.
- Kill switch without state preservation. Fix: checkpoint and record termination reason.
- Measuring attempted steps as completed value. Fix: separate attempts and success.
