| Area | Check before launch |
|---|---|
| Scope | The first intents, languages, channels and exclusions have named owners. |
| Scope | Quality, latency and cost targets use a recorded baseline and risk assessment. |
| Integration | The test app uses separate credentials and cannot modify production records. |
| Integration | Webhook signatures, duplicate events and out-of-order events have passing tests. |
| Integration | Accepted work survives a worker restart. |
| Integration | Public replies, notes, sender identity and destination pass helpdesk readback. |
| Conversation | A customer correction prevents stale replies and stale actions. |
| Conversation | Human takeover stops pending replies and scheduled work. |
| Identity | Anonymous and claimed identities cannot access private account data. |
| Identity | Resource ownership and sensitive-action rules run on the server. |
| Knowledge | Source edits, deletions and permission changes reach search within the agreed time. |
| Knowledge | Conflicting, absent and incomplete evidence produces a safe response. |
| Tools | Each enabled write has idempotency or an explicit reconciliation procedure. |
| Tools | Lost acknowledgments do not cause blind action retries. |
| Security | Logs, traces and test exports exclude secrets and follow the data policy. |
| Security | Prompt-injection and cross-account access tests pass for the chosen scope. |
| Reliability | Timeouts, retries, cost limits and queue limits have one agreed policy. |
| Reliability | Each outage has a tested fallback, safe delay or human route. |
| Evaluation | Critical functional tests and the representative offline evaluation pass. |
| Evaluation | Judge limitations, coverage and segment results are visible. |
| Operations | Support can investigate, take control and pause the service. |
| Operations | Alerts and incidents have owners, runbooks and working contact routes. |
| Metrics | Metric denominators, observation windows and unknown outcomes are defined. |
| Release | The deployed version, configuration and downstream behavior are confirmed. |
| Release | The initial cohort, observation period, stop rules and rollback are ready. |
AI support agent playbook
Templates, checklists and code for your AI support agent
The launch checklist, the operating calendar and every file the 15 guides use. Copy them, adapt them to your own policies, and test them somewhere safe before they touch customers.
Launch checklist
Pass every check with evidence, not a meeting note, before customers depend on the agent. Read the launch guide
Operating calendar
The agent needs owners after day one. Urgent problems never wait for the calendar; the reviews turn what you learn into tested changes. Read the improvement guide
| When | What to review | Owner | What to do | Evidence to keep |
|---|---|---|---|---|
| Continuous / daily | Delivery, outages, security incidents, action outcomes | Engineering | Watch queue age, failed sends, failed handovers, unknown writes, error rates, cost spikes and sync failures. Urgent action does not wait for a review meeting. | An incident or reconciliation record with confirmed downstream state. |
| Weekly | Conversation quality | Support and quality | Review a stratified sample of resolved, transferred, reopened and low-score cases. Find causes, assign fixes and add regression cases. | A prioritized issue list with owners and evidence. |
| Weekly | Knowledge and candidate learning | Knowledge owner | Review stale content, sync gaps, contradictions and learnings. Approve, reject or edit changes. Verify indexed content after publication. | A change log and tested answers for affected cases. |
| Weekly | Agent configuration | Support and engineering | Review task misfires, repeated questions, tool failures, clarification and handover quality. Check recent edits against the fast test pack. | A reviewed configuration release or a recorded decision to keep it. |
| Monthly | Model and retrieval evaluation | Engineering and quality | Review new candidates and deprecations. Screen useful options, then use the offline suite. Start A/B testing only if evidence and traffic justify it. | A comparison with quality, safety, cost, latency and a decision. |
| Monthly | Security and access | Security and engineering | Review dependencies, tool scopes, prompt-injection tests, secrets, retention, access and provider data terms. Follow incident urgency for critical changes. | An access review and closed or assigned security actions. |
| Monthly | Metrics, judge and cost | Quality and service owner | Check denominators, score coverage, judge calibration, language/topic segments and full service cost. Sample raw events to confirm dashboard numbers. | A metric-quality note and budget/capacity action list. |
| Quarterly | Recovery and service scope | Service owner | Exercise recovery, rollback and human takeover. Review ownership, removed content, unused tools, vendor exit and build-versus-buy economics. | A recovery record and a revised service plan. |
| Every release or material change | Focused regression and rollout | Engineering | Test affected contracts and security boundaries. Pin versions, verify the deployed result, observe the cohort and retain rollback. | A release receipt linked to tests and customer-visible readback. |
Downloads by guide
The four Python examples run locally with no network calls and check order access, webhook signatures, response filtering and metric arithmetic. They are teaching files, not a complete agent. Prompts, datasets and worksheets are starting material: no model-quality result is claimed.
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Plan your AI support agent architecture
- service-plan.mdTemplate (Markdown or text)
- source-register.csvSpreadsheet worksheet (CSV)
-
Plan what your AI support agent will cost to run
-
Connect an AI agent to your helpdesk
- intercom_signature.pyRunnable Python example (synthetic data, no network calls)
- event-policy.csvSpreadsheet worksheet (CSV)
- channel-contract.csvSpreadsheet worksheet (CSV)
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Handle conversation state, corrections and human takeover
- conversation-state.csvSpreadsheet worksheet (CSV)
- event-policy.csvSpreadsheet worksheet (CSV)
-
Authenticate users and enforce AI tool permissions
- order_status_example.pyRunnable Python example (synthetic data, no network calls)
- order_status_prompt.txtTemplate (Markdown or text)
- order_status_tool_schema.jsonJSON fixture or schema
-
Build and maintain an AI agent knowledge base
- source-register.csvSpreadsheet worksheet (CSV)
- knowledge-change-test.mdTemplate (Markdown or text)
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Design RAG retrieval and reranking for support questions
- retrieval-cases.jsonJSON fixture or schema
- source-register.csvSpreadsheet worksheet (CSV)
-
Design AI support prompts, guidance and task workflows
- task-contract.jsonJSON fixture or schema
- handover-template.mdTemplate (Markdown or text)
- selection-cases.jsonJSON fixture or schema
-
Build safe tools and APIs for an AI support agent
- safe_tool_adapter.pyRunnable Python example (synthetic data, no network calls)
- operation-receipt.jsonJSON fixture or schema
- order_status_example.pyRunnable Python example (synthetic data, no network calls)
-
Give support teams an AI agent management console
- settings-register.csvSpreadsheet worksheet (CSV)
- release-record.mdTemplate (Markdown or text)
-
Secure an AI support agent and handle service failures
- incident-runbook.mdTemplate (Markdown or text)
- data-copy-register.csvSpreadsheet worksheet (CSV)
- failure-test-plan.mdTemplate (Markdown or text)
-
Evaluate AI support agents with tests and live experiments
- evaluation-cases.jsonJSON fixture or schema
- experiment-plan.mdTemplate (Markdown or text)
- selection-cases.jsonJSON fixture or schema
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Measure AI support resolution, CSAT and service quality
- metric-dictionary.csvSpreadsheet worksheet (CSV)
- metric_example.pyRunnable Python example (synthetic data, no network calls)
- decision-record.jsonJSON fixture or schema
-
Launch an AI support agent with evidence and rollback
- launch-checklist.csvSpreadsheet worksheet (CSV)
- release-record.mdTemplate (Markdown or text)
- incident-runbook.mdTemplate (Markdown or text)
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Maintain an AI support agent and add safe self-learning
- learning-candidate.jsonJSON fixture or schema
- weekly-review.mdTemplate (Markdown or text)
- maintenance-calendar.csvSpreadsheet worksheet (CSV)
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