ai-agent-sla-and-customer-commitments

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

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, [phone].

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

1. Read this SKILL.md first.
2. Load references/routing.md to select the absorbed child reference that matches the task.
3. Load only the selected child reference files needed for the current request.
4. 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.

Consolidated Child References

• Load references/routing.md to map retired AI child skill slugs to their reference modules.