Monitoring and Observability for Production AI Agents

What to Monitor

Enterprise AI agents require monitoring at three levels: operational health (is the agent running?), behavioral quality (is the agent doing the right thing?), and business outcomes (is the agent achieving the intended results?).

Operational Metrics

• Request volume — how many interactions per period
• Response latency — how long each interaction takes end-to-end
• Error rate — what percentage of interactions result in errors
• Skill call success rate — which Skills are failing and how often
• Escalation rate — how often the agent routes to a human

Behavioral Quality Signals

• User feedback (thumbs up/down, explicit ratings)
• Escalation reasons — are users escalating because the agent can't help, or because they don't trust it?
• Out-of-scope request frequency — how often are users asking for things outside the agent's scope?
• Conversation abandonment — are users giving up mid-conversation?

Alerting

Configure alerts for: error rate above threshold (5% is a reasonable starting point), latency above acceptable limit, Skill call failure rate spikes, and unusual usage patterns (a sudden spike in requests might indicate the agent is being used in an unintended way).

The Audit Trail as an Observability Tool

Workato's unified audit trail is the primary observability tool for governed agent behavior. Every tool call, every Skill execution, every approval event is logged. Build dashboards over the audit trail to surface behavioral patterns — the audit trail is richer than traditional application logs because it captures the full context of each agent interaction.