Hue builds fake workspaces so AI agents can make real mistakes safely

Hue is building simulated versions of workplace applications so teams can test AI agents without letting them experiment on a real inbox or customer database. Its environments preserve state, giving an agent a world it can change and developers a starting point they can replay.

The YC Fall 2026 startup's launch covers simulated tools such as Gmail, Slack, Salesforce, Notion and QuickBooks. Rather than handing an agent an empty test account, Hue populates the environment with data intended to resemble the history and business context it would encounter at work.

That context is the point. An agent sending an email, updating a CRM or preparing a refund has to reason about information already present in connected systems. A mocked response may exercise an API call without reproducing the circumstances that caused the agent to choose it.

Hue offers two ways to create a test world. A developer or coding agent can describe an organization and its applications, and the platform generates a populated environment. Alternatively, a team can supply traces from a real agent run so Hue can reconstruct the relevant state and reproduce a failure.

Watch Hue's original demonstration.

Once a world exists, an agent can operate against it without affecting live accounts. Developers can return to the same starting state, try different versions of an agent and compare results. Hue also describes setting up quantitative evaluations to track whether changes improve performance.

This makes reproducibility an important part of the product, not simply isolation. If a failure depends on a particular combination of messages and records, a fresh but empty sandbox won't necessarily reproduce it. Keeping the relevant conditions available gives a team something more concrete to check after changing a prompt, tool or model.

Co-founders Aneesh Kethini and Josh Le have worked on agents in operational settings. Kethini was a product manager for Datadog's AI SRE agent and previously a quantitative trader at SIG. Le worked on legal agents at August and has engineering experience at Apple, Gusto, SendBlue and Wayfair.

The founders say their previous roles involved building testing infrastructure, turning production usage into replayable scenarios and generating representative data. Hue packages that kind of work for other teams rather than requiring each one to construct its own testing worlds.

The launch doesn't provide an independent measure of how faithfully its simulated applications match their live counterparts. That matters because an agent can perform well in a simulation that omits an important constraint or behaves differently from the real tool. Generated business history is useful only to the extent that it exercises the situations the team needs to test.

Hue is seeking teams whose agents already work across third-party applications and whose existing tests rely on mocked APIs or unrepresentative sandbox tenants. Its immediate proposition is a controlled place to repeat the messy work, change the agent and see whether the same situation now ends differently.

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