What makes a Workforce agent different from other AI agent platforms?

The short version: many other AI agent tools give you a workflow builder; Workforce gives you an employee.

Most “build your own agent” platforms are built for a specific pattern: you (or a designated “builder” on your team) define, template, and configure an agent for a known, repeatable workflow, then it runs in that provider's cloud, reaching your tools only through whatever connectors exist for them.

• An employee, not a workflow. You don't build or configure a Workforce agent, you onboard it like a hire and hand it open-ended, ambiguous work across your whole team. It figures out the workflow; you don't have to define it first.

• Its own computer, not a connector sandbox. It uses a real computer, browser, desktop apps, files, so it can operate any tool a person could, including ones with no API or connector at all.

• Its own identity, not a shared org asset. It shows up as its own teammate with its own login across your stack, attributable and independently revocable, rather than a bot instance the whole org shares.

• Managed and forward-deployed, not DIY. Automat's own engineers make sure it delivers on your messiest real workflows; you're not the one building, debugging, or maintaining it.

• Model-agnostic, not locked to one lab. We route to whichever model or tool fits the job, you're never locked into a single provider's stack.

Where other agent-builder platforms genuinely win: a simple, well-defined, single-tool workflow that's cheap to template and doesn't need to touch anything outside their connector catalog. Workforce is built for the rest, work that's ambiguous, spans your whole team, or needs to touch a system nobody's built a clean connector for.