AI agent environments
An AI agent environment is a realistic copy of the systems an agent works in, with masked or synthetic data, the business context it needs and the same starting point for every run. We build them from your own SAP, CRM, documents and messages, so you can test, train and compare agents before they touch live systems.
For AI and platform teams, agent builders, SIs and AI labs.
Availability
Why environments
An evaluation tool tells you whether an agent's answer looks right. An environment tells you whether its actions are right: it gives the agent real business context to act on, a known correct outcome, and the same starting state every time.
Reads the traces and the end state, then scores each run: did the agent take the right action?
What you can do
The same environment follows an agent from first prototype to every later release.
Validate agents on realistic scenarios, then rerun the same suite before you promote a new version.
Resettable environments with scored tasks for reinforcement learning, fine-tuning and model comparisons.
Mask and synthesize the documents, prompts and traces that agents and LLMs see, from the first prototype on.
Test Data AgentGenerally available
It works out which records and system states a test needs, then generates, masks or subsets production-representative data, with referential integrity and business rules intact.
Compare
Most teams start with one of the first three. Each solves part of the problem; an environment is built to solve all of it.
| Approach | Real business context | Consistent across systems | Same start every run | No real personal data | Known right answer | Runs in CI/CD |
|---|---|---|---|---|---|---|
| Copy of production | Yes | Yes | Partly | No | No | No |
| Hand-written fixtures | No | Partly | Yes | Yes | Yes | Yes |
| LLM-written test data | Partly | No | Partly | Yes | No | Partly |
| AI agent environment | Yes | Yes | Yes | Yes | Yes | Yes |
Evaluation frameworks aren't in the table because they don't supply data: they score runs on whichever of these you put underneath.
Results
Agent environments use the same masking, subsetting and generation engine our customers rely on for test data today.
Glossary
We've got you covered
A realistic copy of the systems an agent works in: masked or synthetic records, the business context around them and the documents and messages that refer to them, with a reset point so every run starts from the same state.
Give it a realistic environment, a set of scenarios with known right answers, and a reset before every run. Run the scenarios, score the outcomes with your evaluation tool, add adversarial cases, then rerun the same suite before every release.
A copy puts real personal data into prompts, traces and logs, and resetting a full copy between runs is slow and expensive, so runs rarely start from the same place.
The Test Data Agent and TDM for SAP are generally available. Masked SAP test systems for agents, with reset through the API, and the 48-hour proof for documents are available today; emails, chats and PDFs are delivered engineer-led. SAP RL environments are in early access.
Evaluation frameworks measure how an agent performs. The environment is what it performs in: the data, system states and scenarios. It works alongside whichever evaluation tool you already use.
SAP first, with particular depth in finance, procurement and supply chain. The platform also covers Salesforce, Oracle, Workday, Microsoft D365, ServiceNow and the major databases, plus emails, chats and PDFs that match the same records, delivered engineer-led today.
No. It runs on-premises, in private cloud or hybrid, under your existing identity, network, security and governance controls.
Yes. Provisioning is triggered through REST APIs and CI/CD pipelines, so every release can rerun the same scenario suite. See regression testing for agents.
Next step
We build one environment for a process you choose, run your agent in it and show a reset between two runs.
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