Launching an AI agent is the start of the operating work, not the end of it.
When an agent starts handling real customer conversations, the important question is no longer only whether the model can produce a plausible answer. The team also needs to know whether the customer's request was completed, whether follow-up is required, and whether the same issue is likely to happen again.
What operations teams need to see
Production agent reviews should make unresolved work obvious:
- conversations where the agent stopped before the task was complete
- cases where a customer needed a handoff or callback
- tool failures that created follow-up work
- patterns where operators keep correcting the same behavior
These are operational signals, not just model traces.
Recovery matters as much as detection
Finding a failed interaction is only useful if the team can recover the customer outcome. That might mean assigning a review, creating a ticket, retrying a workflow, or notifying the team that owns the next step.
The useful loop is straightforward: detect the issue, recover the work, capture the correction, and review whether the agent should change.
Corrections should become durable improvements
Operator corrections are valuable because they describe what should have happened in production language. A good improvement workflow preserves that context and turns it into something testable before anything changes in the agent.
That is the operating model FieldSignal is being built around.