AI agents promise to automate multi-step operational work, but a reckless pilot can create more incident volume than value. The key is to treat agent pilots as production experiments: small scope, controlled blast radius, and explicit guardrails.
Start with narrow workflows that already have measurable SLAs and clear exception paths—think tier-1 support responses, document validation steps or basic fulfilment checks. Require human-in-loop checkpoints for anything that triggers irreversible actions, and log every agent decision with the context it saw.
Use evaluation harnesses to replay historical cases before touching live traffic. Probe for prompt injections, malformed tool responses and edge cases in underlying APIs. Only graduate to partial automation once false-positive/false-negative rates and latency are visible and acceptable to the business owner.
Success looks like this: the pilot improves a specific KPI, incidents stay flat or decline, operators trust the telemetry, and there is a clear path to scale (or to shut it down). That discipline builds the organisational muscle to roll out more ambitious agents with confidence.