AI Agent Observability Platform
Trace every agent run end to end — replay prompts, model output and each tool call, track cost and failure rate, and turn production incidents into regression evals
Interactive tool will be available soon
Meanwhile, read the guide below to understand how it works
Features
- ✓ End-to-end tracing of every step in every run
- ✓ Step-by-step view of prompts, model output and tool arguments
- ✓ Token cost, latency and failure rate broken down per agent
- ✓ Turn real failure cases into a regression eval set
- ✓ Alert on quality and cost anomalies with linkage to the exact version
How to Use
- Instrument your agent app with a one-line SDK
- Inspect runs and replay them step by step in the dashboard
- Add failure cases to an eval set and run regressions
- Set alert thresholds for quality and cost
FAQ
What is agent observability?
A class of monitoring and debugging platforms built specifically for AI agents. They record the full trajectory of each run — every prompt, model output, tool call, latency and cost — so you can answer why it failed and where the money went.
How is it different from regular APM logs?
APM shows latency and status codes but no semantics. Agent observability captures full prompts and model output, reconstructs the decision process step by step, and tracks model-specific metrics such as token cost.
Will it slow down production?
Ingestion is usually asynchronous with minimal impact on the request path, and sampling controls volume. Latency-critical systems can enable full tracing only on high-value paths.
Can it trace multi-agent collaboration?
Yes. Platforms commonly stitch the runs of a single task into a call tree so you can see who invoked whom, how context moved and which step dominated total time.
How long is data retained?
Most platforms keep data for a default period with configurable retention; enterprise plans can export to your own object storage or warehouse for internal and compliance needs.
Is it free?
Pricing is usually per trace or run with a free tier that is enough for individual developers to debug.