Real-time observation of AI agent behavior, performance, and health — including conversation quality, error rates, and resource usage.
Agent monitoring is observability for AI. You watch what the agent is doing, how well it is performing, and whether anything is going wrong. This includes conversation quality metrics, tool call success rates, error rates, latency, and resource consumption.
Unlike traditional application monitoring (CPU, memory, uptime), agent monitoring also tracks behavioral metrics: is the agent giving good answers? Is it triggering too many approvals? Is it using tools efficiently?
An unmonitored agent is a black box. You discover problems only when customers complain. Monitoring catches issues proactively — before they affect users.
Clawctl provides a monitoring dashboard with agent health, conversation metrics, approval rates, and error tracking. Alerts notify you when agents need attention.
Try Clawctl — 60 Second DeployAgent health, response times, tool call success rates, approval rates, error rates, and conversation counts.
Yes. Configure alerts for error spikes, health check failures, and unusual activity.
Near real-time. Dashboard updates within seconds. Alerts fire within minutes of anomaly detection.
Audit Trail
A chronological record of every action an AI agent takes, providing accountability, compliance evidence, and forensic capability.
Health Checks
Automated probes that verify an AI agent is running, responsive, and functioning correctly at regular intervals.
Cost Optimization
Strategies for reducing LLM and infrastructure costs when running AI agents without sacrificing quality or reliability.
Model Routing
Directing different agent tasks to different LLM models based on complexity, cost, or speed requirements.