A Startup Raised $10 Million to Build Practice Copies of Workplace Software for AI Training

Arga Labs announced a $10 million seed round on August 26, 2026, led by General Catalyst with participation from Box Group, Emergence, Gradient, and SV Angel (TechCrunch).

The company, co-founded by CEO Philip Li, builds exact digital copies of workplace software — Salesforce, Workday, email systems — so that AI agents can practice in them before being let loose on the real thing (TechCrunch).

Think of it like a flight simulator for AI. Before a pilot flies a real plane full of passengers, they train in a simulator that behaves exactly like the cockpit. Arga wants to give AI agents the same kind of safe practice space — a copy of Salesforce or Workday that looks and behaves like the real system, without the risk of touching actual customer data or disrupting live business operations.

The problem is one that anyone working with AI in business settings has run into. An AI agent that can read an email, update a customer record, or file an expense report in a test environment often behaves differently when it touches a live system with real customer data, sales reps managing quotas, and legal compliance requirements. The gap between a demo that works and a system reliable enough for everyday use is where most business deployments currently stall.

Arga's approach is to close that gap not by making smarter AI models but by making better practice environments. The idea is that AI agents fail in real-world use not because they lack intelligence but because they have not trained against settings realistic enough to prepare them.

Yuri Sagalov, a managing director at General Catalyst who runs the firm's seed program, commented publicly on the need for testing tools like Arga (TechCrunch). The group of investors backing Arga spans firms with expertise in enterprise software, early-stage AI, and consumer technology, reflecting that Arga is both an infrastructure project and a business application.

The technical challenge Arga has taken on is significant. Building an accurate copy of a platform like Salesforce is not a one-time job. Salesforce alone has hundreds of data types, a complex permissions system, and updates three times a year. Workday is customized differently by every company that uses it. Email systems vary widely in how they handle routing, shared mailboxes, and archiving. Keeping these copies accurate as the real software changes is ongoing work, not something you build once and ship.

There is also competition to consider. The companies that make this software — Salesforce, Workday, Microsoft — are building their own AI agent platforms with their own built-in training tools. Salesforce's Agentforce, for example, runs agents inside Salesforce's own environment. Arga's pitch depends on companies wanting a neutral training tool that works across multiple platforms, something no single software vendor will offer.

The broader context here is that AI agents are moving from research labs toward real business use, and the tools needed to test and evaluate them are largely missing. The past three years of investment produced smarter AI models, but the infrastructure to make them dependable in everyday use is still being built.

What Arga has announced is an early-stage company with a clear idea and credible investors. The $10 million gives them time to prove that these software copies can be built accurately enough to make AI agents genuinely more reliable. Whether that idea holds up under the engineering pressures of keeping multiple platforms accurately copied and fending off competition from the software vendors themselves will depend on work that has not yet happened.

The promise, if it holds, is straightforward: AI agents that have practiced in realistic environments before touching real business data, reducing the failures that make AI deployments risky today.