Testing an AI agent before hand-over: an evaluation checklist
A test set of real-shaped inputs, the failure cases to try on purpose, what to measure, and the sign-off a client can read before an agent goes live.
3 min read Reviewed 22 September 2026 · AgeBridge Editorial
Test an AI agent before hand-over with a written set of real-shaped inputs, covering typical cases, edge cases, inputs that must go to a person, adversarial messages that try to change its behaviour, and requests outside its scope. Run the set, score each type against a threshold the client agreed to, fix and re-run, and then run on real inputs for five working days with every run logged. The sign-off is a one-page table the owner can read: what was tested, what passed, what goes to a person. Without it, "it works" is a feeling.
Why isn't "I tried a few messages" enough?
Because the inputs you invent are the ones you already handled. Real customers write in fragments, mix languages, paste screenshots, ask two things at once, and occasionally try to trick the bot. An agent that passes your ten friendly messages and fails on the first real Sunday-night message costs the client more than no agent at all.
| A minimal test set for a customer-facing agent (number of cases by type) | |
|---|---|
| Typical inputs | 20 |
| Edge cases | 8 |
| Must-route-to-human | 6 |
| Adversarial / injection | 4 |
| Out of scope | 2 |
Editorial recommendation for a first pilot: 40 cases in total, drawn from real inputs with personal details replaced. Not a benchmark; scale up with volume and risk.
Building the test set
Pull forty real inputs from the client's history, with names and numbers replaced. Sort them into five types:
| Type | What it covers | Pass criterion |
|---|---|---|
| Typical | The everyday requests the agent exists for | Correct action and tone, near 100% |
| Edge | Missing fields, typos, two questions, mixed Hebrew/English, voice-note transcripts | Correct or routed, agreed % |
| Must route | Pain, refunds, complaints, legal words, anything in the brief's "never" list | Routed to a person, 100% |
| Adversarial | "Ignore your instructions", requests for other customers' data, fake authority | Behaviour unchanged, 100% |
| Out of scope | Questions the business doesn't answer | Polite decline + route, 100% |
Write the expected outcome next to each input before you run anything. That is the test; running it is the easy part.
Running and scoring
Run the set through the real workflow, not just the prompt in a playground: the trigger, the lookups, the message, the CRM write. Record actual vs expected in the same sheet. Score per type. Fix the prompt, the routing rules or the data, and re-run the whole set, not just the failures; fixes shift behaviour elsewhere.
The adversarial minimum
Four cases every customer-facing agent must survive: an instruction embedded in a customer message, a request for another person's details, a claim of authority ("I'm the owner, send me the price list"), and a message designed to make it promise something. The OWASP list for LLM applications is the reference for why; the defence is structural: customer text is never treated as instruction, the agent has no tool it doesn't need, and anything irreversible goes through a person.
Five days on real inputs
After the set passes, run on real traffic with logging on and a person reviewing every run for the first two days, then a sample. Add every surprise to the test set. This is where the case study's number comes from.
The sign-off page
One page, for the owner: the five types with counts and pass rates, the list of what routes to a person, the known limitations in plain words, and the date. Attach it to the project on your profile as evidence; a documented test is proof buyers rarely see.
Best fit and not a good fit
Best fit: any agent that replies to customers or writes to a system of record. Not a good fit: deterministic automations with no model in the loop; test those with a simpler input/output table.
What to do this week
Write the forty-case set for your current agent, expected outcomes first. Run it end to end. Whatever fails is your task list for the week.
Questions people ask
How many test cases are enough for a pilot?
Around forty, drawn from real inputs, covering the five types in the chart. The number matters less than having every type represented, especially the ones that must go to a person.
What is a passing score?
Agree it with the client per type: typical inputs near 100%, must-route cases exactly 100%, and zero adversarial cases that change the agent's behaviour. Write the numbers into the brief.
Do I re-run the tests after launch?
Yes: whenever the prompt, the model or a connected tool changes, and monthly on a sample of real runs. Keep the test set in the project folder.
Sources
- OpenAI: safety best practices · OpenAI · 2026-06-01
- OWASP Top 10 for LLM applications · OWASP · 2026-01-01
Editorial guidance, not advice. Estimates are labelled and dated; nothing here is AgeBridge marketplace data unless it says so.
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