Use cases
AI agent evaluation for every team that ships agents
Autessa Evaluations is used by engineers who build agents, product and QA teams who decide when to ship, support leaders who own quality, and risk teams who guard what agents say. Here is how each one uses it, by role and by industry.
By role
Who uses Autessa Evaluations, and for what?
AI and agent engineers
Build test sets for each agent, run them on the draft before every publish, and read failing cases down to the tool call that went wrong.
Product managers
Write expected outcomes in plain English, watch version performance after each release, and decide when to roll back from numbers, not anecdotes.
QA and test leads
Turn every reported failure into a test case, run large suites by sampling, and keep a pass-rate record per version in eval history.
Support and CX operations
Label conversations with your team’s judgment, track success rates by channel, and see what checks caught each day.
Risk, compliance and security
Block made-up facts, personal data, credentials and leaked instructions before they are sent, and keep a record of what each check did.
Data and analytics teams
Sync conversations and evaluations into tables, grade transcripts from other systems in batch, and pull statistics by version over the API.
By industry
Agent evaluation by industry
| Industry | How teams use it |
|---|---|
| Customer support | Rehearse refund, billing and escalation conversations with simulated customers, and make sure no amount or order number is ever made up. |
| Financial services | Keep account and card numbers out of replies with detectors that check card checksums, and hold replies for AI review where policy is strict. |
| Healthcare and benefits | Hold replies that give medical advice for review, and grade calls on what the caller actually heard. |
| Public sector | Test residents’ questions in several personas before launch and keep full logs of every conversation for review. |
| Retail and ecommerce | Catch wrong prices, delivery dates and promo codes before they are sent, and compare versions after every catalog change. |
| Software and SaaS | Grade support transcripts from any system through the API, and run agent test sets from release scripts. |
The loop
Test, ship, score, guard, repeat
Most teams use every area together: test sets before a publish, AutoEval and human labels after, response checks on every reply, and Transcript Grading for agents that run somewhere else.
Agent testing
Autessa Evaluations tests an AI agent before launch by running test sets as real conversations, driven by simulated users, and grading every one.
Scoring and judges
Every evaluated conversation is scored on four quality dimensions and, when you describe what should happen, judged against that expected outcome.
Production monitoring
In production, every conversation is logged in full and can be scored automatically, labeled by people and compared version by version.
Response checks
Response checks evaluate each reply before it is released, so a made-up price or a leaked card number never reaches the customer.
100% customizable, code and all
Make Autessa Evaluations fully your own.
Use it out of the box, then change anything. Ask Autobot, Autessa’s AI builder, for a new field, screen, report, workflow or agent and it builds it into your copy of the app. Or open the code and edit it yourself. Nothing is locked: the app, its data and its code are yours.
Start free and make it yours