SLAs and accountability for AI agents in production
By Equipo Quantum Developers
Summarize:
When an agent moves to production, it stops being an interface and becomes operating capacity. That is why it needs SLAs, clear owners, and evidence for every relevant decision.
What an agent SLA should cover
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Response time to classify or execute a task.
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Minimum output quality: accuracy, completeness, and format.
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Maximum exception time without an owner.
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Workflow availability and continuity plan.
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Mandatory evidence for sensitive decisions.
Accountability by layer
The business owner is accountable for the rule and outcome. Technology owns integration, security, and availability. The automation team owns observability, changes, and agent operation. If those boundaries are not written, everyone assumes someone else is watching.
Incident example
A purchasing agent approves an order with a blocked vendor because master data was stale. A useful SLA does not ask only whether the model failed; it asks who owned the data, why the exception was not detected, and what evidence exists to reverse the action.
Controls in Quantum
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Record execution, affected object, and rule version.
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Configure alerts for exception aging.
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Force human approval at critical thresholds.
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Review performance weekly before expanding scope.
Recommended decision
Do not scale an agent without owner, exception threshold, and review routine. Speed without accountability becomes operational debt.
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