Arga Raises $10 Million to Build Resettable Enterprise AI-Agent Sandboxes

Enterprise AI agents are being asked to work across systems where one action can change the conditions for the next. Arga has raised $10 million to build controlled replicas of that software, giving developers a place to train and test agents before they operate against live business systems.

General Catalyst led the seed round, joined by Box Group, Emergence, Gradient and SV Angel. Arga builds training environments for enterprise software including Salesforce, Workday and email clients.

The software must behave like work

Arga’s product is not a single test API. It aims to create a digital twin of an enterprise program: a replica that preserves the software’s structure, permission systems and webhooks. A webhook is an automated message a system sends when a specified event occurs.

That distinction centers on state, or the record of what has already happened in a software environment. Arga says its replicas preserve permissions, webhooks, side effects and service state, allowing an earlier agent action to shape the next step in a workflow. Its public product includes both API-only replicas and services that reproduce APIs and user interfaces.

Because Arga controls the environment, developers can reset and modify it, then run many copies at once. The goal is to test an agent across interactions between programs rather than treating each application as an isolated task.

Repetition is the bottleneck

One intended use is reinforcement learning, a technique that can run the same scenario tens of thousands of times while retaining successful strategies. Enterprise applications are difficult to clone or reset at that scale, which limits this form of training outside a controlled environment.

Philip Li, Arga’s CEO and co-founder, offered a sales example: a prospective client is entered in Salesforce while a colleague contacts the same company through HubSpot. The agent must recognize that the records concern one company, avoid sending a duplicate email and choose the appropriate contact.

A controlled copy is not automatically a faithful one

Arga’s environments are designed to replicate software such as Salesforce and HubSpot without using live production data. That separates repetitive testing from sensitive corporate systems, where security risks, operational mistakes and the difficulty of resetting software states can make large-scale testing impractical.

The critical product question is fidelity. A replica that misses a permission rule, webhook sequence or API quirk could produce a passing test that does not hold up in production. Maintaining accurate replicas also becomes harder as the number of supported services grows and those services change their interfaces and behavior.

Selling the practice layer

Arga has put pricing around the infrastructure, listing a free tier, a Pro plan at $1,250 per month and a Team plan starting at $3,500 per month. Its challenge is to show that its simulations are useful enough for enterprise teams to pay for and to expand integrations without sacrificing realism.

The company is entering a competitive enterprise AI infrastructure market that includes robotic-process-automation providers and large cloud software platforms. Data privacy and intellectual-property rules are also evolving, leaving Arga’s commercial case tied both to technical accuracy and to proof that customers can deploy agents with greater confidence.