March 9, 2026
Using AI agents for operational support, not hype
A grounded approach to applying AI agents where they can meaningfully reduce coordination overhead.
AI agents are most useful when scoped to specific operational tasks such as summarizing updates, drafting runbook steps, or preparing handoff context.
Avoid general-purpose, open-ended mandates. Define clear inputs, expected outputs, and escalation rules so humans can trust and verify the result.
Local or constrained-context models can be a strong fit for sensitive internal workflows where data control matters. In all cases, include traceability.
Measure impact with operational metrics: turnaround time, rework rate, and queue stability. If those numbers do not improve, the agent should be redesigned or removed.
AI becomes valuable when it quietly improves execution quality. Treat it as infrastructure, not marketing.