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Test agents before they touch the business.

Three things, in increasing order of how much they will convince you: a sandbox with synthetic data, a dry run that tells you what the agent would have done yesterday, and a simulation against last month's real history. None of them sends anything or changes a record.

Demo data

What it would have done

Sales Follow-up Agent · Against last month's leads · nothing was sent

Would have received a first email82
Would have needed a second attempt31
Would have been escalated to a human14
Would have been left alone — already in an open conversation16
Leads in the period143

82 + 31 + 14 + 16 = 143. No email was sent and no record was changed.

A dry run against yesterday

"It would have emailed 17 customers and asked for 4 approvals." Concrete, checkable, and produced from your actual records rather than a description of intent.

Simulation against a month of history

Run the agent against last month's leads and see the whole distribution: how many would have been contacted, escalated, or left alone. This is the panel that gets an agent approved.

Nothing escapes the simulation

No message is sent, no record is written, no connected system is touched. Simulated runs are recorded as simulated and cannot be mistaken for real ones in the audit trail.

Evaluations for the systematic version

Named test suites — does it refuse an unauthorised refund, does it escalate on conflicting information, does it stay inside a customer's scope — with pass rates, runnable in your pipeline.

Questions this answers

The things you currently have to ask somebody.

Each of these is a question an owner asks regularly and currently answers from memory, a spreadsheet, or by interrupting whoever is nearest.

What would this agent have done if we had switched it on last week?

How do I know it will refuse something it should refuse?

Can I test it without any risk of contacting a real customer?

Did this change make the agent better or worse?

Where this stops

What it does not do.

A simulation replays what your records say happened; it cannot tell you how a customer would have replied. It shows the actions the agent would have taken, not the outcomes they would have produced. A dry run is also only as representative as the period you ran it against — a quiet week proves little. And a passing evaluation suite is evidence, not a guarantee: it tests the cases somebody thought to write.

Next

Where this connects.

Give AI a job.

Build an agent, choose what it can access, define what it may do, and deploy it to work safely across your business.

No code required · Full SDKs and API · Stop any agent instantly · [email protected]