Meet the agent command center for fast-moving teams.
Three pillars, one dashboard, one alert layer, two lines of code. Spend tells you what every agent costs. Pulse catches a dead agent before your customers do. Verify catches quality drift before your users do. Start with the one that hurts most today.
Spend tells you what every agent really costs.
Pulse catches a dead agent before your customers do.
Verify catches quality drift before your users do.
AI agent observability is the practice of monitoring production AI agents for cost, reliability and output quality, with one record per agent run. Traditional tools watch requests and errors, so they miss what an agent run cost, whether it ran, and whether the answer was any good.
| What you run today | What it shows | What it misses for AI agents |
|---|---|---|
| OpenAI / Anthropic dashboards | Total token usage and spend | Cost by agent, customer, feature or run |
| Logs and error tracking | Exceptions and stack traces | Missed runs, AI cost context, output quality |
| Cron and uptime monitors | Whether a job fired | What the run cost, produced or retried |
| APM (Datadog, New Relic) | Request latency and throughput | The agent run as a unit: goal, quality, spend |
| Spreadsheets | Manual, after-the-fact cost | Real-time alerts and run-level detail |
Start free. Paid plans include a 14-day trial. No per-seat tax, no card to start.