Monitoring an AI agent means checking whether it completes the right work, follows its permissions, and produces an outcome someone can trust. A successful model response alone does not show that the business task finished. Build monitoring around the complete request, from arrival to acceptance or escalation.
Define a completed business outcome
Write a completion rule that an operations colleague can verify. For a service-request assistant, this might mean a valid summary attached to the correct request and accepted by a reviewer. Record rejected, abandoned, and unresolved cases separately. Otherwise, a system that stops early can appear fast and inexpensive.
Choose a small dashboard: accepted completion rate, correction rate, unresolved cases, total processing time, and cost per accepted case. Break the results down by workflow version and case type. Record denominators alongside percentages so a small sample does not look more convincing than it is.
Keep a trace of the workflow
Use a request identifier to connect model calls, tool calls, handoffs, validation results, and the final business status. OpenAI’s evaluation guidance describes traces as an end-to-end record that can support structured checks of tool selection and handoffs.
For each step, capture the component version, timing, result status, and reason for escalation where available. Capture the minimum data needed to investigate failures. Restrict access to logs, define retention, and avoid copying credentials or unnecessary personal information into telemetry.
"In the past, we managed software; today, we design autonomous processes that AI executes for us. The future belongs to those who automate not just the tasks, but the decision-making itself."
Review quality alongside system health
Watch technical signals such as timeouts, queue growth, repeated tool failures, and unusual call volume. Then review a sample of completed work against a written rubric: factual accuracy, correct record selection, required fields, and compliance with the workflow’s action limits. Include difficult and rejected cases in the review.
Automated scoring can help sort cases for inspection, but validate its judgments against human review. Maintain a fixed test set for comparing releases and add newly discovered failure patterns. A lower error count is meaningful only when the cases and measurement method remain comparable.
Give every alert an owner and an action
Define what requires immediate intervention and what belongs in a scheduled review. An unauthorized action attempt may justify pausing a workflow; a slower response may require checking a dependency. Choose thresholds from the process’s impact and expected behavior, then adjust them using observed results.
NIST’s AI Risk Management Framework Core calls for ongoing monitoring and clear responsibilities. Turn that into a practical incident record: affected requests, containment, recovery, customer impact where relevant, and the test that will check the fix. Review trends with the process owner and keep a manual route available when the agent cannot finish.
I look forward to seeing how these developments will improve service levels and customer satisfaction in the freight industry!