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A handover checklist for production AI integrations
An AI integration is not handed over when code is merged. The receiving team needs enough context to run it, spot a failure, recover safely and know who owns each decision. Agree on the handover package before the final delivery week so missing access or operating knowledge is not discovered at the last minute.
What the receiving team needs
- System boundary: a current diagram of the user flow, data sources, model or external service, storage, logs and downstream systems. Mark which data may cross each boundary.
- Configuration and owners: deployment steps, environment variable names without secret values, model and prompt identifiers, schema or index version where relevant, and named owners for application, credentials and vendor relationship.
- Operations: common failure signals, location of redacted diagnostics, safe retry limits, manual fallback, stop mechanism and steps to restore the previous working version.
- Acceptance evidence: scenarios that passed, known gaps, agreed quality and latency limits, cost assumptions and actions that require human review.
- Transfer: a working session where the receiving operator completes a normal run and one recovery step, then records open questions and responsibility boundaries.
Worked example: support-summary integration
Illustrative example: a project adds draft summaries of support conversations to an existing internal queue. The handover diagram identifies the source record, summarization service and draft field; it makes clear that the feature neither sends a reply nor changes ticket status. The runbook covers an unavailable source, where to inspect redacted error IDs, and how to disable the feature while preserving the original conversation.
Before transfer, the receiving owner checks one normal summary and one unavailable-source case, confirms the previous configuration can be restored, and identifies who can rotate the service credential. This is an example handover pattern, not a result or capability claim about a particular client.
Handover decision checklist
- Can the team name accountable product and technical owners?
- Are data flow, access, logs, retention and external dependencies documented?
- Can an operator detect, stop and recover from likely failures?
- Are model, prompt, schema and evaluation versions tied to the accepted build?
- Are human review, cost boundaries and known limits explicit?
- Has the receiving operator completed a normal run and recovery step?
See the AI feature runbook guide and ongoing operations notes. Our AI services page describes the scopes currently offered.
Written by the Clutch Developer studio in Barcelona. Questions: info@clutchdeveloper.com