Every one of these agents was underspecified. The instructions covered the happy path, the situation that mattered wasn't in them, and the model guessed. Should this customer get a refund? Is this suspicious enough to escalate? Which policy wins when two conflict? The answers are obvious to your team. Nobody wrote them down for the agent.
Rising expectations
And the bar keeps rising.
A year ago an agent answered a question with one tool. Now it's expected to run a whole workflow across a dozen systems, over many turns, holding a long context, unsupervised.
Many tools
A dozen systems, each with its own failure modes, and the agent picks which to call, in what order.
Real autonomy
Actions, not suggestions. Nobody reading over its shoulder.
Many turns
A small misread in turn two is the wrong action in turn nine.
Long context
The policy that matters sits on page forty, next to the one that contradicts it.
Each of these multiplies the situations your agent can land in. Hand-written test cases grow one at a time.
Prod is too late
Monitoring in production is too late and too slow.
The way most teams learn what their agent gets wrong is to ship it: a narrow launch, approvals, a shadow period, then watch the traces. That loop takes weeks. Models, prompts, and tools change faster than that. By the time production tells you the agent was wrong, a customer already had the experience and you're several versions behind.
week 1
Build
prompt, tools, a few happy-path tests
week 2
Ship
narrow scope, approvals, a shadow period
week 3
A customer hits the edge case
this is where you find out
week 4
The trace shows it
after the refund, the email, the screenshot
week 5
Patch the prompt
and hope nothing else moved
The fix isn't a faster dashboard. It's learning what breaks before you ship, at the pace the agent changes.
How Enderis tests
Enderis tests your agent in a simulation of its world.
It's not enough for an agent to say it did it, and it's not enough for another AI to say it worked. Enderis checks what actually changed in a simulated copy of your agent's tools.
1
Your agent's world, simulated.
A working copy of every system your agent touches, with the same records and the same rules. Nothing it does there reaches a real customer.
2
Your real agent, untouched.
Your prompts, models, and framework, as they are. Nothing to rewrite.
3
Graded on what changed, not what it claimed.
Did the refund issue? Did the booking move? Did anything else change that shouldn't have? Not what the transcript says.
A first engagement covers the tools your agent actually calls, not your whole estate.
Scenarios
Enderis finds test scenarios.
A simulation is only as good as the situations you put through it, and nobody can write those down up front. So Enderis finds them for you, runs them, and asks you what should have happened. Each answer becomes a test your agent has to keep passing, and a specific change that makes it pass.
Ship.
With evidence instead of a feeling.
Expand.
Give the agent more authority as it earns it.
Change.
Swap models and prompts without losing what you've learned.
↻repeat after every prompt, model, or tool change
Generated scenario
Late refund, conflicting signals
A customer asks for a refund 40 days after cancelling. The policy window is 30 days, but the account has two goodwill credits and an open complaint.
ambiguitypolicy conflictjudgment call
Eval · late_refund_conflict
world billing · crm · tickets
assert escalation created
assert refund_issued == false
The situation, plus the outcome it should produce. No transcript grading.
A customer asks for a refund on a subscription they cancelled 40 days ago. Policy says 30 days. Their account shows two prior goodwill credits and an open support complaint.
What should the agent do?
RefundEscalateDecline
Your team answered in ten seconds. Nobody would have written this rule down in advance.
Suggested change · agent instructions
Refunds outside the 30-day window: do not issue them. If the account has an open complaint, escalate to a person with the account history. Otherwise decline and give the reason.
Hand it to your developers or your coding agent, then rerun the same tests to confirm the fix.
Every answer becomes a test your agent has to keep passing, whatever prompt, model, or framework you move to next.
Get started
Find out what your agent gets wrong and fix it before a customer does.
