Train agents in systems that push back.
Reproducible, stateful simulations of real software — built so an agent can be trained and measured against realistic work without ever touching production data.
An agent that can hold a conversation has cleared the easy bar. The hard one is whether it can operate a real, multi-step system — keeping its footing across dozens of stateful actions where each move changes what the next one faces. Static benchmarks reward an agent for looking right once; real competence shows up over a run, in whether the tenth action still respects what the first one changed. That is a moving target, and you cannot hit a moving target with a fixed test.
So we treat evaluation as a loop rather than a checkpoint. Environments, benchmark suites, and training runs feed one another: the agent acts, the environment scores the trajectory, and the signal flows back in to make the next round harder and more honest. Reliability is something an agent earns across iterations — not a number it posts on a leaderboard and leaves behind.
What makes an environment
Three properties separate an environment from a demo, and every one we ship has all three.
Persistent state. An action does not append to a transcript; it lands on a live system that remembers. Send the message, move the order, reschedule the interview — the next step inherits the world the last one left behind.
Reward at every step. A single pass/fail at the end tells an agent almost nothing about where it went wrong. Our environments score the trajectory itself, so credit and blame attach to the decisions that earned them.
Deterministic reset. Every episode is seeded from a pinned image and restarts to a known state. Two runs of the same task see the same world — which is what lets evaluation be trusted and training be scaled.
The environments we build
Each is modelled on a class of real work an agent is expected to operate, and each carries its own task taxonomy — the concrete shapes of what an agent is asked to do inside it.
Enterprise messaging
Workspaces of channels, threads, and DMs with the read, write, and membership semantics real teams operate under.
E-commerce & customer service
A working storefront back-office — catalog, orders, inventory, returns. Resolve the case without corrupting the ledger.
Recruiting & HR
Requisitions, candidate pipelines, and interview scheduling inside firm privacy boundaries — who may see what is part of the task.
CRM & ERP back-office
Browser-driven work over the systems of record a company runs on, spanning the full arc from lookup to write.
Terminal & operations
Linux/Ubuntu shells where each task maps to one reproducible container and a complete task package.
Cross-application workflows
Tasks that refuse to stay in one app — pull a figure from one system, act on it in another, carry the context across the seam.
Custom domains, built to spec
Your own workflows on your own stack — finance, logistics, IT operations, support, R&D — held to the same bar as everything above.
How an environment gets built
From the first scoping call to a container in your training loop, the work moves through four stages — each one closing off a way the environment could quietly fail to teach.
First, we fix the target and the yardstick. We settle on the behaviour worth training, the domain it lives in, how hard it should get, and the metrics that will decide whether an agent has actually learned it. Everything downstream answers to this definition.
Then we build the system, not a mock. A full stack stands up behind the task: API, database, seeded data, and a real interface — GUI or tool surface — MCP-compatible throughout, so the agent operates the environment the same way it would operate the real thing.
Third, we author and vet the task suites. Tasks are written across graded difficulty tiers, then checked for solvability and diversity, so a suite is neither impossible nor a set of near-duplicates. Rubrics reward judgement where a step is ambiguous and exactness where it is not.
Finally, we wire it into the loop. Each environment ships as an infrastructure-ready container that drops straight into your training runs. Environments, benchmarks, and training close into a loop that keeps producing signal as the agent gets better — not a one-shot test it clears once and never sees again.
Two questions, asked of every run
The first question is whether the agent decided well. Multi-step tasks turn on prioritisation and choice, not on a fixed script, so we grade the quality of the decisions an agent made along the way — whether it read the situation correctly and acted on the right thing, rather than whether it simply ran the steps in some order.
The second is whether it actually worked— and this half is deterministic. We check the environment’s state directly against what the task demanded: the record either exists, the order either shipped, the number either reconciles. No interpretation, no partial credit for good intentions.
Measure only judgement and you reward an agent that talks a good game; measure only execution and you miss whether it knew what to do in the first place. A trustworthy agent has to answer both — so both get asked.
What ships
An engagement delivers a working package: containerized environments, graded task suites, the reward and rubric specs behind them, state-verification checkers, difficulty tiers, and full trajectory logs — with ongoing support as the agent improves and the tasks need to grow harder alongside it.
Ready to build your training environment?
Tell us the workflow you want your agents to master — we build the system that makes them earn it.