How to Monitor AI Agent Performance

Log every input, reasoning step, tool call, output, and error in a structured format (JSON). Include timestamps, request IDs, agent identifiers, latency per step, token counts, and cost per request. Store logs in a searchable system — Datadog, a Supabase table, or even a well-structured log file for smaller deployments.

The goal is being able to answer any question about any agent interaction within minutes. 'Why did the agent send this email to the wrong person on Tuesday at 3 PM?' You pull the log, see the inputs, trace the reasoning, and identify the root cause. Without structured logging, debugging production issues becomes guesswork.

Create dashboards that show: task completion rate (percentage of tasks the agent completes without human intervention), average response time (latency from input to output), error rate (percentage of tasks that fail or require escalation), cost per task (average LLM tokens and API calls per completed task), and volume (total tasks processed per hour/day/week).

Refresh these dashboards every 5-15 minutes. The operations team should be able to glance at the dashboard and know immediately whether the agent system is healthy. I use a simple Next.js dashboard connected to a Supabase table that aggregates agent metrics.

Set alerts for: error rate exceeding 5% over a 1-hour window, average latency exceeding 2x baseline, cost per task exceeding 3x average, task volume dropping below 50% of expected (may indicate the agent is stuck or the trigger is broken), and any security-related event (unauthorized tool calls, data access violations).

Alerts should go to Slack, Telegram, or PagerDuty — wherever your team actually looks. Configure severity levels: informational (something's unusual), warning (investigate within an hour), critical (fix now). Most alert fatigue comes from too many informational alerts. Be aggressive with critical alerts and conservative with everything else.

Automated metrics tell you if the agent is performing within bounds. Manual spot-checks tell you if the quality is actually good. Review 10-20 randomly selected agent interactions per week. Score them against your quality rubric: Was the response correct? Was the tone appropriate? Did the agent follow its instructions? Were the right tools called?

Track quality scores over time. A gradual decline in manual quality scores often precedes a spike in automated error metrics. Catching the quality drift early lets you fix prompts and logic before users notice.

Monitor cost per task, tokens per request, and total monthly spend. Break costs down by agent, by task type, and by LLM model. Identify the most expensive operations and evaluate whether a cheaper model, a cached response, or a restructured prompt could reduce cost without sacrificing quality.

Set monthly budget alerts at 50%, 75%, and 90% of your target spend. Cost overruns in agent systems are usually gradual — a prompt change that increases token usage by 20% goes unnoticed until the monthly bill arrives. Real-time cost tracking prevents surprises.