Adept AI
By Adept AI
Adept AI was an AI research company focused on building 'action models' — AI systems trained to operate software interfaces and complete multi-step digital tasks on a user's behalf, rather than only generating text.
Definition
Adept AI was an AI research company focused on building 'action models' — AI systems trained to operate software interfaces and complete multi-step digital tasks on a user's behalf, rather than only generating text.
Overview
Adept positioned itself around a specific vision within the broader AI agent space: instead of a model that only outputs text, an action model observes a software interface (such as a web app or internal tool) and directly performs the clicks, form fills, and navigation needed to complete a task described in natural language. Its flagship research model, ACT-1, was demonstrated operating real software applications end-to-end based on natural-language instructions. This approach sits within the wider category of agentic AI — systems that plan and take actions rather than just respond conversationally — and shares conceptual ground with browser-automation and computer-use features later built into major LLM platforms. Adept's premise was that enterprises have large libraries of existing software (CRMs, internal tools, ERPs) that would be more valuable if an AI could operate them directly rather than requiring custom API integrations for every task. Adept raised substantial venture funding from major investors and was seen as one of the more prominent independent labs working on action-taking AI. In 2024, a significant portion of Adept's leadership and technology was absorbed into Amazon in a licensing and talent arrangement, a pattern similar to other AI startup acquihires that year, reshaping the company's independent trajectory. It is often mentioned alongside Foundation Model in this space.
Key Features
- Focus on 'action models' that operate software interfaces directly
- ACT-1 research model demonstrating end-to-end task completion in apps
- Natural-language instructions translated into interface actions
- Positioned to work across existing enterprise software without custom APIs
- Backed by significant venture capital investment