The eval platform for autonomous software

If you want to know what an agent would do with access to payment, support, or source-control systems, one way to find out is giving it access. Unfortunately, in the status quo, that is often the only way you can know.

Testing action-based agents before they interact with real services requires stateful environments that behave like those services. Text-only evaluations can describe the environment in the prompt. Agents that can change real systems need more than that.

Agents can now write to databases, trigger payments, and push code to production. But teams today have no safe way to test them. The only way to know what an agent would do in production is to put it in production, and this necessarily means that you can only discover failures after the damage is done.

Archal solves this by creating stateful clones of software services at scale, so agents can be tested against realistic environments before deployment. These clones carry the business logic, object relationships, and edge cases agents need to exercise before they enter production.

The same infrastructure we built is useful for more than agents. Any software that creates tickets, sends messages, or implements business logic against real services ought to be tested, but mocks are insufficient. By simulating software worlds, Archal changes what developers can feasibly test.

Lack of trust represents the core bottleneck. Archal is building the platform to help developers and businesses know exactly what happens when AI is treated as more than a chatbot, and when software is allowed to act directly on the services their business depends on.