platoseed
Real-world sandboxes to test agents and agent-facing software
We build environments to test your app and agents against the real world. Deploy your code/agent into a sandbox that runs against replicas of external services (Stripe, Slack, Google Drive, etc.). Twins support the same APIs, MCP tool calls & SDKs as the real software, and you can spin up thousands of instances in parallel and seed them using natural language. Run real workflows, surface edge cases, and observe how your code/agents actually behave before hitting production.
Arga Labs provides real-world sandboxes that run your code against twins of third-party APIs, MCPs, and CLIs so apps and agents can interact with the real world safely.
Arga Labs creates real-world sandbox environments by simulating or duplicating external APIs, MCPs, and CLIs, allowing AI systems and agents to execute against authentic interfaces without risking live systems. This enables testing and interaction with real-world behaviors in a controlled sandbox before deployment.
Who it’s for: Organizations building AI agents or agent-facing software that need safe, realistic integration testing with external services
Co-founder & CEO @ Arga Labs. Built an internal dev tool @ Amazon to automate complex engineering workflows, resulted in 10+ recurring weeks/year of engineer hours saved across multiple teams, and prevented VP escalations due to bug issues. Before that, I was a fencer on the Canadian Junior National Team and researched cognitive systems at UBC
Co-founder, CTO @ Arga Labs Prev. SDE @ Stripe, quant @ GS Skipped high school, graduated @ 19 Pro player for Identity V, quit because I had to finish my finals :(
Production-mirror sandboxes with digital twins of every SaaS integration
Arga Labs provides production-mirror sandbox environments for testing multi-app agents by spinning up twins of external integrations like Stripe and Slack while routing other services to production. Developers or AI agents can automatically generate tests, run them, and receive validated results without corrupting production data.
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