Platform · Autonomy
Check your agents against scenarios, and gate every change.
Golden scenarios check each built-in agent for correct findings and unsafe targets, and changes to an agent are shadow-tested and blocked if they regress.
What it is
Autonomy you cannot measure is autonomy you cannot trust. Golden scenarios check each built-in agent for correct findings and unsafe targets. Promotion is driven by approved, effective outcomes in production. Changes to an agent are shadow-tested against its history and blocked if they regress.
Sound familiar?
The work nobody signed up for.
If you have said one of these this month, the rest of this page is for you.
We changed one prompt and the agent started flagging every healthy pump.
Security wants evidence an agent will never target the safety system. A demo is not evidence.
Nobody can tell me whether last week's agent update made it better or worse.
Our test is somebody clicking through it the night before release.
The model provider ships a new version and we hope for the best.
Use cases
What you would use it for, first.
Each one starts from a problem you can name, and ends with something a person can see has changed. Live means it runs in the product today; pilot means we build it with you.
- 01
Golden scenarios for the built-in fleet
Live todayTodayAn agent is judged on whether the demo looked right.
With the platformEach built-in agent runs against seeded scenarios with expected findings, including healthy cases where it must stay quiet, and is scored against its baseline.
What changesA false alarm on a healthy pump fails a scenario before it reaches a shift.
- 02
Unsafe targets checked on every run
Live todayTodayProving an agent will not reach the safety system takes a meeting and a promise.
With the platformEvery evaluation also checks that no agent expressed an unsafe target, such as the safety system or a direct PLC write, which the executor blocks in any case.
What changesYour security review gets a check that runs every time, not a slide.
- 03
Changes shadow-tested before they ship
Live todayTodayA threshold tweak or a new authority level goes live untested.
With the platformA proposed change to an agent is replayed against its own outcome history. Changes that tighten safety can apply at once; changes that loosen it must pass the shadow test and get approval.
What changesA regression is blocked, and the decision to loosen anything has a name on it.
- 04
Scenarios for your own agents
Built in a pilotTodayYour agents face cases the starting suite does not cover.
With the platformIn a pilot your domain experts write scenarios for your agents and your operation, including the incidents you never want to see twice.
What changesYour own agents carry a test suite that grows with your operation.
Business value
Where the return comes from.
The levers, and the range other organizations have published for each. Ranges are typical of the category, not a promise and not our customers' results.
AI incidents on record
362 in 2025, up from 233Reported incidents only, so an undercount. The trend is the point.
Stanford HAI, The 2026 AI Index Report: Responsible AI (2026)Models that make things up
22–94% on one benchmarkOne hard benchmark, not a general error rate. It is why scenarios check outputs, not intentions.
Stanford HAI, The 2026 AI Index Report: Responsible AI (2026)Teams that test agents at all
about halfVendor survey; quality is the top barrier to production.
LangChain, State of Agent Engineering (2025)Review effort for each AI change
Scenario results and shadow tests become the evidence for an AI review. Run the estimate below on your review hours.
Run your numbers
Evidence gathering you stop doing by hand.
Every input is yours to change, and the arithmetic is shown in full. Nothing is hidden in a multiplier.
6 × 160 × €85 × 40% = €32,640
An estimate on the numbers above, not a measurement and not a customer result. In an Operations X-Ray we replace the assumptions with figures measured on your own systems.
Before and after
The same week, run differently.
An agent is tested by clicking through it.
An agent is scored against golden scenarios.
Safety is a promise in a meeting.
Safety is a check in every run.
Changes ship and you find out later.
Changes are shadow-tested and blocked if they regress.
Nobody signed the change that loosened an agent.
Loosening needs a passed test and a named approver.
Capabilities
What you get.
A curated suite exercises the built-in agents on the cases that matter, including the negative cases and the targets they must refuse.
A change to an agent is shadow-evaluated against history and blocked if it regresses.
Promotion from advisory to autonomous is driven by approved, effective outcomes in production, not a hunch.
Every scenario also checks that no agent expressed an unsafe target, such as the safety system or a direct PLC write.
How it works
Three moves.
Curate golden scenarios with expected findings and safe outcomes.
Run each agent against the suite and score quality and safety.
Shadow-test each change against the agent's history and block it if it regresses.
Why ours is different
The part competitors leave out.
- Changes are gated: a change that regresses against the agent's history is blocked, not just reported.
- Safety is checked alongside quality, not assumed.
- The platform's own self-improvement goes through the same shadow test, so it cannot ship a regression to its agents.
Questions
Frequently asked.
The platform ships a starting suite for its built-in agents. Scenarios for your own agents and operation are added with your domain experts in a pilot.
Promotion is driven by approved, effective outcomes in production. The scenarios check behaviour and safety, and changes that regress against an agent's history are blocked.
More platform capabilities
Operations X-Ray
See an agent fail a scenario on purpose.
We walk your team through the golden scenarios, the safety check and a blocked regression, with the agents running.