How to evaluate whether an AI agent is working well
To evaluate an AI agent running in a real process, measure what people do after it acts. Correction rate, escalation rate and reopen rate say far more than an impression of quality, and all three are available from the process itself.
Why agent evaluation is usually subjective
An agent is assessed by reading some of its outputs and forming a view. That view is shaped by the examples chosen, which are rarely representative, and by whoever built it.
Meanwhile the process is already producing the objective measure: what humans had to do afterwards.
Questions worth answering:
- How often does someone correct what the agent did
- How often does a case escalate after the agent handled it
- How often is a closed case reopened
- Which categories does it handle worst
- Is performance changing over time
Metrics that reveal performance
| Metric | What it shows | Where it comes from |
|---|---|---|
| Correction rate | How often a human changes its output | Record history |
| Escalation rate | Cases it could not handle | Process stages |
| Reopen rate | Premature closures | Reopened records |
| Coverage | Share it handled at all | Volume comparison |
| Performance by category | Where it is weakest | Grouped analysis |
| Trend | Whether it is drifting | Over time |
Step by step to evaluate
- Establish a baseline from before the agent was introduced.
- Measure correction rate, using the record history.
- Track escalations after the agent handled a case.
- Watch the reopen rate, which catches premature closures.
- Break results down by category, since averages hide the weak areas.
- Narrow the scope where it performs poorly, rather than disabling it entirely.
- Re-measure monthly, since inputs change over time.
Why coverage and accuracy must be read together
An agent handling five percent of cases perfectly is easy to report as a success and is not worth much. One handling sixty percent with a small correction rate is far more valuable even though it makes more mistakes in absolute terms. Neither number means anything alone.
Why choose Jestor
In Jestor, the agent acts within field permissions you define, the record history distinguishes changes made by an agent from those made by a person, escalations and reopens are stages in the process, and usage is measured in the platform, so evaluation comes from data the process already produces.
Frequently Asked Questions
What is the best single measure of an agent?
How often a human has to correct what it did. See jestor.com.
Why analyse by category?
Because an average hides the categories where performance is poor.
What should I do when it performs badly in one area?
Narrow its scope there rather than disabling it entirely.
Video Tutorial: Step by Step
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