M4A

Adept

AI agents that use software tools and APIs to automate knowledge work

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Memory Types

episodic, procedural, task-context

Integrations

web-browsers, apis, software-tools


Overview


Adept is building AI agents that can use software tools and APIs just like humans do, enabling automation of complex knowledge work. Having raised over $400 million from top-tier investors, Adept is developing both the foundational models and agent platform needed to turn natural language commands into actions across software applications.


The company's vision is to create "AI teammates" that can understand user intent, plan complex workflows, and execute tasks across multiple applications. Adept's ACT-1 model is trained specifically for action-taking rather than just text generation, making it uniquely suited for agent applications.


Key Features


  • **Action Transformer (ACT-1)**: Model trained specifically for taking actions
  • **Software Control**: Agents can use any software interface
  • **Multi-Step Planning**: Break down complex tasks into executable steps
  • **API Integration**: Work with APIs and web services
  • **Natural Language Interface**: Describe tasks in plain language
  • **Learning from Examples**: Few-shot learning of new workflows
  • **Cross-Application**: Work across multiple software tools
  • **Real-Time Execution**: Watch agents work in real-time

    • When to Use Adept


    Adept is ideal for:

  • Enterprises automating complex knowledge work
  • Organizations with repetitive multi-step workflows
  • Teams wanting AI that can use existing software
  • Companies requiring automation across multiple tools
  • Knowledge workers needing AI assistants for complex tasks
  • Research teams exploring agentic AI capabilities

    • Pros


  • Massive funding ($400M+) from top investors
  • Novel approach with action-specific models
  • Can work with existing software without APIs
  • Ambitious vision with strong team
  • Real automation rather than just text generation
  • Potential to transform knowledge work
  • Strong research foundation
  • Backed by proven founders

    • Cons


  • Still in early stages (beta/limited access)
  • Not yet widely available
  • Unproven at scale in production
  • Likely expensive when released
  • Limited public information on capabilities
  • May require significant training for specific use cases
  • Reliability concerns for critical workflows
  • Long-term viability questions despite funding

    • Pricing


  • **Not Yet Public**: Limited beta access only
  • **Expected**: Enterprise pricing when launched
  • **Contact**: Waitlist for early access

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