Arga Labs has raised $10 million in seed funding to expand its infrastructure for testing and evaluating AI agents that interact with real-world software systems. The round was led by General Catalyst, with participation from BoxGroup, Emergence, Gradient, and SV Angel. The company plans to use the capital to accelerate research and development, expand its evaluation platform, and improve how quickly it can create software replicas for agent testing.
Testing AI Agents Beyond Traditional Software
Arga argues that conventional software-testing methods are poorly suited to autonomous agents because agents operate non-deterministically and can take actions across multiple external tools. Unlike traditional applications, AI agents may call APIs, change data, respond to events, manage permissions, and retry failed operations. As these systems gain broader access to business software, Arga says the risks associated with unreliable behavior increase alongside their capabilities.
The company has built real-world sandboxes based on high-fidelity twins of external software services. These environments are designed to reproduce backend behavior, authentication, permissions, webhooks, mutable resources, timing conditions, and failures without requiring developers to test directly in production. Customers can interact with the twins through API, command-line, and Model Context Protocol interfaces while monitoring how an agent changes the simulated software.
Building Stateful SaaS Twins
Arga says its approach differs from lightweight API mocks because each twin maintains the internal state of the service being replicated. The company also supports multi-application scenarios in which synchronized versions of tools such as Slack, Stripe, and Jira can operate together using shared data. This is intended to help developers evaluate workflows that cross several applications, where failures may be harder to identify with isolated testing.
According to Arga, it can currently clone a software-as-a-service product in less than 12 hours while reproducing its backend functionality and behavior. The company says customers have run more than 100,000 twin instances during the past 16 weeks. Arga is now working to reduce the time required to generate these environments from hours to minutes.
Benchmarking Multi-App Agent Performance
Arga is also developing ArgaBench, a benchmark designed to measure how AI agents perform across interconnected software environments. The company said internal testing found that leading models could complete much of a multi-step task but still fail when required to verify information across different applications without explicit instructions. Arga plans to publish the full benchmark and related research findings separately.
Because Arga controls the state of its simulated services, the company says it can capture execution traces, anomalies, and errors generated during agent interactions. Those records are intended to help teams identify where an agent failed and determine what needs to be improved in its reasoning, tool use, or workflow design. The broader goal is to give developers better visibility into agent behavior before deployment.
Funding the Next Phase
Arga plans to use the seed financing to expand technology that automatically generates high-fidelity SaaS twins as agents gain access to a wider range of public APIs. The company expects future agents to discover services, interpret schemas, create accounts, and combine tools in ways developers may not anticipate. Under that model, automated environment generation would become increasingly important as manually building test systems for every service becomes impractical.
The funding gives Arga Labs additional resources to develop infrastructure aimed at making autonomous software safer and more reliable before real-world deployment. Its strategy centers on replacing static test cases and shallow mocks with stateful simulations that reproduce the systems agents are expected to use. As companies assign AI agents more complex tasks across critical software, Arga is positioning its platform as a layer for validating those systems before failures reach production.