| name | ai-agent-sla-and-customer-commitments |
| description | Use when defining agent SLAs, customer commitments, SLA dashboards, credits, support promises, and service evidence for agentic AI products. |
| metadata | {"portable":true,"compatible_with":["claude-code","codex"]} |
AI Agent SLA And Customer Commitments
Operating contract
Inputs
| Input | Required | Purpose |
|---|
| Domain evidence | yes | service boundary, customer tier, measurable SLI data, support coverage, exclusions, and credit policy |
Outputs
- Produce: SLA schedule, SLI/SLO definitions, dashboard specification, breach workflow, and customer evidence.
Capability and permission boundaries
Default to read-only analysis. Read only scoped records; redact secrets and regulated data. Writes, execution, network calls, production configuration, customer communication, billing changes, and delegation require explicit authority and an identified owner. Never widen tenant, time-window, or system scope implicitly.
Degraded mode
When required telemetry, evidence, execution, network access, or write authority is unavailable, return a partial result with each unassessed item labelled, preserve the safest existing state, and state the evidence or approval needed to continue. Never convert missing evidence into a pass.
Decision rules
| Condition | Action |
|---|
| Scope, owner, or threshold is missing | Stop the affected decision and request it |
| Evidence is incomplete but read-only analysis is safe | Produce a qualified partial result and gap list |
| A mutation exceeds authority or tenant boundary | Block it and route for approval |
| Evidence meets the stated threshold | Issue the output with provenance and owner |
Anti-Patterns
- Treating absent evidence as success. Fix: mark the check unassessed and name the missing source.
- Expanding one tenant or workflow to all tenants. Fix: enforce supplied scope at every query and action.
- Performing a production write during analysis. Fix: emit a reviewed change plan until authority is explicit.
- Reporting a metric without population, window, or source. Fix: attach all three.
- Hiding a failed threshold inside an average. Fix: report failure slices and the remediation owner.
Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.
Use When
- Define agent availability, completion, response-time, quality, and support commitments.
- Design SLA credit automation and customer-facing service dashboards.
- Map agent task evidence to support, credits, renewals, and account reviews.
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
Consolidated Child References
