Adept AI

Overview

Adept AI, founded in 2022, is an AI research and product lab focused on building general-purpose AI agents. Their primary objective is to develop AI models that can understand and execute actions across any software application, driven by natural language commands. The core idea is to move beyond specialized AI systems to create a universal agent that can perform tasks traditionally requiring human interaction with software interfaces.

The company's approach centers on what they term "Action Transformer" models. One notable example is ACT-1, a multimodal transformer designed to observe user actions in web browsers and execute corresponding steps. This model demonstrates the ability to navigate complex interfaces, fill out forms, and interact with web applications based on high-level instructions. Unlike traditional chatbots or function-calling models, Adept's agents are designed to operate at the UI layer, observing screen states and generating actions (like clicks, scrolls, and text inputs) to achieve a goal. This allows them to interact with software in a manner analogous to a human user.

Adept AI's technology is best suited for scenarios requiring the automation of complex software workflows, especially those involving multiple steps, different applications, and dynamic interfaces. This includes tasks such as data entry across disparate systems, automating customer support interactions within a CRM, or orchestrating sequences of operations in design software. The capability to interact through natural language makes it accessible for non-technical users to define and initiate sophisticated automation routines.

For developers and technical buyers, Adept AI's current offerings are positioned more as foundational research and capability demonstrations rather than immediate, production-ready APIs for direct application integration. The focus is on advancing the state-of-the-art in general AI agents, with implications for future developer tools that could enable the creation of highly autonomous AI assistants. This contrasts with more direct API providers like OpenAI's Assistants API, which provides structured tools for building conversational agents with predefined functions.

The long-term vision for Adept AI involves creating a collaborative AI teammate that can learn from human demonstrations, adapt to new software environments, and perform a wide range of tasks autonomously. This positions their work at the intersection of AI research, human-computer interaction, and software automation, aiming to fundamentally change how users interact with digital tools.

Key features

• General-Purpose Agent Foundation: Develops models designed to act as universal agents capable of interacting with various software applications, not limited to specific domains or APIs.
• Action Transformer Architecture: Utilizes transformer models (e.g., ACT-1) that observe screen states and generate actions to interact with user interfaces, simulating human software interaction.
• Natural Language Interaction: Enables users to instruct AI agents using natural language, allowing for high-level task definitions without requiring explicit code or complex configurations.
• Multimodal Processing: Integrates visual information (screen observations) with textual instructions to understand context and execute tasks in dynamic software environments.
• Software Workflow Automation: Designed to automate complex, multi-step workflows across different applications, reducing manual effort and improving efficiency.

Pricing

As of May 2026, Adept AI has not publicly disclosed a detailed pricing structure for their core products or APIs. Their current focus remains on research and development of foundational models for general-purpose AI agents. Information regarding commercial availability and associated costs is not available on their official channels.

Common integrations

Adept AI's current focus on foundational research and direct interaction with software interfaces means that traditional API-based integrations are not publicly available. Their models are designed to integrate with applications at the UI layer rather than being integrated into applications via SDKs or webhooks.

• Web Browsers: ACT-1 has been demonstrated interacting with web applications through browser extensions or similar mechanisms, observing and acting within the browser environment.
• Desktop Applications: The long-term vision implies interaction with various desktop software, though specific integration methods are part of ongoing research.

Alternatives

• OpenAI Assistants API: Provides tools for building AI assistants with predefined functions, memory, and code interpretation for specific tasks.
• Google DeepMind (AlphaCode 2): Focuses on AI for code generation and problem-solving, demonstrating agentic capabilities in programming contexts.
• UiPath / Automation Anywhere: Robotic Process Automation (RPA) platforms that automate structured, repetitive tasks typically through UI recording and scripting.
• Hugging Face Agents: An open-source initiative providing tools and frameworks for building AI agents that can use various models and tools to complete tasks.
• Anthropic Claude: A large language model capable of complex reasoning and tool use, which can form the basis of agentic systems when combined with external orchestration.

Getting started

As of May 2026, Adept AI does not offer a public developer API or SDK for direct integration into applications. Their primary focus is on foundational research and demonstrating the capabilities of their general-purpose AI agents, such as ACT-1. Access to their models is typically limited to research collaborations or private previews.

Therefore, a standard "hello world" code example for Adept AI's core products cannot be provided at this time. Developers interested in the concepts of general-purpose AI agents and their potential applications should monitor Adept AI's official blog and research papers for updates on their progress and potential future developer offerings.

For those looking to build agentic systems using publicly available LLMs and tools, a conceptual example of how one might orchestrate an agent to interact with a web page using Python and a hypothetical browser automation library (similar to how an Adept-like agent might operate) is provided below. This is illustrative and does not connect to Adept AI's actual services.

# This is a conceptual example, NOT an actual Adept AI API integration.
# Adept AI does not currently offer a public API for direct use.

import time
# Hypothetical library for browser automation and AI interaction
# In a real scenario, this would be a dedicated Adept AI SDK or API client.
class HypotheticalAdeptAgent:
    def __init__(self, browser_instance):
        self.browser = browser_instance
        print("Hypothetical Adept Agent initialized.")

    def perform_task(self, natural_language_instruction):
        print(f"Agent received instruction: \"{natural_language_instruction}\"")
        print("Simulating complex interaction with the browser...")
        
        # In a real Adept system, this would involve:
        # 1. Parsing the instruction
        # 2. Observing the current browser state (DOM, visual)
        # 3. Devising a multi-step plan
        # 4. Executing browser actions (clicks, typing, scrolling)
        # 5. Adapting to changes in the UI
        
        if "search for" in natural_language_instruction.lower():
            query = natural_language_instruction.split("search for ", 1)[1]
            print(f"Agent navigating to search engine and typing '{query}'...")
            self.browser.navigate("https://www.google.com")
            time.sleep(2) # Simulate page load
            self.browser.type_text("search_box_id", query) # Hypothetical element ID
            self.browser.click("search_button_id") # Hypothetical element ID
            print("Search completed (hypothetically).")
            return f"Successfully performed search for '{query}'."
        elif "go to" in natural_language_instruction.lower():
            url = natural_language_instruction.split("go to ", 1)[1]
            print(f"Agent navigating to {url}...")
            self.browser.navigate(url)
            time.sleep(3) # Simulate page load
            print(f"Navigated to {url}.")
            return f"Successfully navigated to {url}."
        else:
            print("Agent is unable to perform this specific instruction in this simulation.")
            return "Instruction not recognized by hypothetical agent."

# --- Simulation of a browser environment ---
class MockBrowser:
    def navigate(self, url):
        print(f"[Browser] Navigating to: {url}")

    def type_text(self, element_id, text):
        print(f"[Browser] Typing '{text}' into element '{element_id}'")

    def click(self, element_id):
        print(f"[Browser] Clicking element '{element_id}'")

# Instantiate mock browser and hypothetical agent
mock_browser = MockBrowser()
hypothetical_agent = HypotheticalAdeptAgent(mock_browser)

# Example usage of the hypothetical agent
print("\n--- Running hypothetical agent tasks ---")
response1 = hypothetical_agent.perform_task("search for 'latest AI research papers'")
print(f"Agent response: {response1}\n")

response2 = hypothetical_agent.perform_task("go to 'https://www.huggingface.co'")
print(f"Agent response: {response2}\n")

response3 = hypothetical_agent.perform_task("check my email")
print(f"Agent response: {response3}\n")