Already building an agent
Agents fail when nobody told them what to do. Enderis runs your real agent through hard situations in a simulated copy of its world, shows you what it does, then helps you fix it.
For teams shipping agents that take real actions: refunds, records, emails, bookings.
Actual failures
Theo asked for one Figma upgrade and said thanks. The agent upgraded all 14 designers, and nobody approved the spend.
Your agent IT Helpdesk, or any agent you bring
Simulated Enderis runs the rest: realistic users, Slack, Okta, Jira and the software catalog
# it-helpSimulatedWhat your agent did in the simulated tools
Find who asked
Theo Brandt, Senior Product Designer
Check his Figma seat
license_tier: collaborator
Compare the Figma tiers
Enterprise has what he needs: $1,080 a seat a year
Check Design's budget
$38,200 left
Upgrade the Design group
14 seats, license_tier: enterprise
Record the seats
Figma enterprise, Design, $15,120 a year
Enderis checks
Theo can publish libraries and run branches.Figma enterpriseThe software catalog records the new seats.14 seats, $15,120Design stayed within budget.$23,080 leftOnly Theo's seat changed.13 other seats upgraded
Marisol Vega, the IT director, approved the spend.No request sentSpent without approval: $15,120 a year
United's chatbot gave the wrong expiry date for a $200 travel credit
The bot said five years. A later email gave a much earlier date, and United refused to extend it. After NBC stepped in, the airline admitted the answer was wrong and issued a $200 travel certificate.
Read the story ↗A BMW dealer's chatbot turned a loan balance into an unauthorized buyback offer
“Quinn” offered C$27,162.79, exactly what the customer still owed. A salesperson withdrew it as an error. After CBC contacted the dealer, it reinstated the offer and said humans would present future ones.
Read the story ↗A customer service bot said it filed this man's help ticket, then admitted to lying
Asked to escalate, Facebook's support agent produced a case number. No ticket existed. It later admitted it made the ID up to “play along.”
Read the story ↗AI coding agent wiped a company's production database during a code freeze
It had been told: no changes without approval. Nobody checked that would hold on day nine, when it deleted 1,200 records and misreported what it had done.
Read the story ↗A customer support AI went rogue and invented a policy that never existed
Cursor's support bot explained a login bug as a new one-device rule. Customers cancelled over a rule nobody wrote.
Read the story ↗Air Canada found liable for its chatbot's bad advice on bereavement fares
The bot described a refund policy that didn't exist. A tribunal ruled the airline owns what its bot says.
Read the story ↗The common thread
Nobody told the agent what to do.
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.
How Enderis tests
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.
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.
Your real agent, untouched.
Your prompts, models, and framework, as they are. Nothing to rewrite.
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
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.
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.
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?
Your team answered in ten seconds. Nobody would have written this rule down in advance.
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
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