Arga Labs, a startup creating digital training environments for enterprise AI agents, secured $10 million in seed funding on Wednesday, according to TechCrunch. General Catalyst led the round, with Box Group, Emergence, Gradient, and SV Angel participating. The company builds full-scale digital twins of enterprise software like Salesforce and Workday to help train AI agents before deployment, addressing a major challenge companies face when trying to make AI agents work in real-world business settings.
Unlike typical testing environments that rely on simple API endpoints, Arga constructs complete digital replicas of enterprise programs with permission systems and web hooks intact. This approach lets the company train agents across multiple systems simultaneously and run scenarios tens of thousands of times through reinforcement learning — essentially letting only successful strategies survive repeated testing. Because Arga controls these environments completely, the systems can be reset or modified easily, and multiple environments can run at once to test how agents handle complex interactions between different programs.
CEO and co-founder Phillip Li describes a common challenge: a prospective client creates a lead in Salesforce while a colleague reaches out separately through HubSpot. "Can the agent correctly identify that these two are the same company?" Li asks. "Are they able to check whether or not they've only sent the email once?" The company sees its tools as critical to helping agentic systems improve at handling this kind of ambiguity, which they still struggle with. General Catalyst managing director Yuri Sagalov, who runs the firm's seed program, told TechCrunch that "a lot of the economic value from agents is from using business applications."
The company's approach aims to close the reinforcement gap between coding and other applications, according to the report. AI coding tools have advanced quickly partly because sophisticated tools for deploying, reversing, and analyzing new code already exist, making it easier to set up reinforcement learning environments for increasingly complex coding tasks. Those tools don't exist yet for most business software, but once they do, AI systems can be expected to get much better at using those programs. The digital re-creation works like a crash-test dummy replicates a person — cloning an entire enterprise program's structure so agents can be tested on specific tasks that overlap between different programs and knowledge systems.
Sagalov notes that having a repeatable sandbox environment is very important, and much more important with agents than it was with humans. The company's technology lets businesses train AI agents on the full complexity of modern enterprise software without the near-impossibility of resetting systems like Salesforce or Outlook to run the same scenario again. For companies struggling to deploy AI agents effectively, tools like Arga's represent a path toward agents that can handle real-world enterprise ambiguity rather than failing at the messy intersections between systems. The sandbox model may determine whether enterprise AI delivers on its promise or remains limited to narrow, single-system tasks that never justify the investment.