Use case
Catch AI agent drift before it breaks your workflow
You deploy an agent on Tuesday and it works perfectly. By Thursday, the outputs are subtly wrong, but it's still returning 200s. The LLM provider updated their model, a downstream API changed, or your prompts decayed. Your agent is drifting, and you have no alarms.
How the agent runs it
- 01
The agent ingests your production traces and session logs for observability.
- 02
It runs scenario-based simulations and unified evaluations against a known baseline.
- 03
It detects model drift, prompt drift, or data drift and triggers an alert.
You get a clear alert when your agent's behavior shifts, so you can fix it before users notice.
Want this on your systems?
Common questions
- What exactly is AI agent drift?
- Drift is when your agent's behavior changes over time without any code change. It's not a crash, just a silent shift in output quality or logic.
- How does the monitoring agent detect the drift?
- It uses session-level observability on live traffic and runs regular scenario simulations to compare current performance against a known good baseline.
- Does this work with tools like Arize or Phoenix?
- Yes. The agent is built to work with the observability platforms and evaluation frameworks you already use in production.