Thesis

If you want to know what your OpenClaw would do if it had your bank account information, one way you can find out is by giving it your bank account information. Unfortunately, in the status quo, it is also the only way you can know.

Testing action-based agents before they interact with the real world services is something that requires a perfect simulacra of those services. Testing is easy with agents that are only text retrieval based: you just give the model the environment in the form of input text. For agents with the potential to cause real damage, that isn’t enough.

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 and edge cases that make them indistinguishable from the real services to AI agents, meaning agents and harnesses can be tested 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.