Arga raises $10M to build digital twins for enterprise AI agent training

Arga, a startup building training environments for enterprise AI agents, announced Wednesday that it has raised $10 million in seed funding, led by General Catalyst with participation from Box Group, Emergence, Gradient, and SV Angel. The company aims to solve a critical bottleneck in AI adoption: the difficulty of training agents to handle complex, real-world business software like Salesforce, Workday, and email clients.

Why enterprise AI agents are hard to train

Most current testing environments for AI agents offer a stateless API endpoint, which doesn’t capture the full complexity of enterprise systems. Arga takes a different approach by building a full-scale digital twin of the software—cloning the entire environment, including permission systems and web hooks. This allows for more robust training across multiple systems, according to the company.

Philip Li, CEO and co-founder of Arga, illustrated the challenge with a common scenario: a prospective client is created as a lead in Salesforce while a colleague reaches out separately through HubSpot. The agent must correctly identify that both refer to the same company, check whether an email has already been sent, and determine which contact to address—tasks that require understanding context and ambiguity.

Digital twins and reinforcement learning

Training AI agents typically involves reinforcement learning (RL), where scenarios are run tens of thousands of times to reinforce successful strategies. However, enterprise software like Salesforce or Outlook cannot be easily reset or cloned, making such scale nearly impossible. Arga’s digital twin solves this by providing a fully controllable environment that can be reset, modified, and run in parallel to train agents on complex interactions between different programs.

This approach mirrors the success seen in AI coding tools, which benefit from sophisticated deployment and testing infrastructure. By creating similar tools for business software, Arga aims to close the reinforcement gap and accelerate AI capabilities in enterprise applications.

Investor perspective and market relevance

Yuri Sagalov, managing director at General Catalyst, noted that the economic value of AI agents lies in their use of business applications. “Having a repeatable sandbox environment is very important, and much more important with agents than it was with humans,” he told Bitcoin World. This investment reflects a growing recognition that robust testing infrastructure is essential for AI agents to operate reliably in enterprise settings.

Conclusion

Arga’s $10 million seed round underscores the emerging demand for infrastructure that can train AI agents effectively. By providing realistic, controllable training environments, the company is positioning itself at the forefront of enterprise AI enablement. As AI agents become more integrated into business workflows, tools like Arga’s could prove critical to their success.

FAQs

Q1: What is a digital twin in the context of AI training?
A digital twin is a virtual replica of a software system that includes its structure, permissions, and integrations. It allows AI agents to be trained in a realistic but controlled environment that can be reset and modified as needed.

Q2: Why is reinforcement learning difficult for enterprise software?
Reinforcement learning requires running thousands of scenarios, but enterprise software cannot be easily reset or cloned. This makes it nearly impossible to test agents at scale without a digital twin environment.

Q3: Who invested in Arga’s seed round?
The round was led by General Catalyst, with participation from Box Group, Emergence, Gradient, and SV Angel.