Boost your productivity. Let Atlas do your tasks for you, autonomously.
Autonomy & Independence:
Autonomous AI systems are built to work independently, by taking tasks over for users. We see these systems most prominently today in autonomous vehicles (eg. Waymo). Rather than improving productivity while completing a task, autopilot aspires to give users the entirety of the time back for a given task.
Atlas can tackle higher-complexity tasks (eg. System II type problems) that require a series of ‘chained’ decisions to be successfully completed. To be autonomous, autopilot requires next-generation technology including intent analysis, logical reasoning, simulation environments utilizing a customized LLM, and the ability to self-verify each decision.
Self-Learning & Dynamic Planning:
Autonomous AI agents can experiment with various tactics to solve a given problem by using a safe simulation environment where they can learn which tactics succeed. They generate dynamic plans – just like human thoughts – and write custom Python code in real-time for each experiment, conducting self-verification at each step. If these agents are unable to successfully self-verify a winning solution, or come up with multiple options to solve the problem, they will confer with the users to make a final decision.
To operate autonomously with reliability and accuracy, Atlas needs to infer certain decisions based on the users’ stated and learned preferences. This could be things like a preferred airline, medium of communication, favorite cuisine, and much more. This personalization means less monitoring, oversight and more hours saved for users and their businesses.
Assistance and Support:
Copilots assist users while performing tasks. Co-pilots do a great job providing suggestions, giving aid in solving problems, and conducting on-demand research. With co-pilots, the final decision on “how” to complete a task rests with the users.
Copilots will collaborate with users to provide data-driven insights and suggestions to cut down task time. The users then evaluate these inputs and make a final decision on the path forward. Copilots can tackle simpler tasks (eg. summarizing a document) and complex tasks (e.g. estimating the calorie count of a food image), but can’t tackle problems requiring sequential sub-tasks and decisions known as “chaining”.
The most powerful copilots can learn from interactions and user inputs, creating early layers of personalization. This in-turn helps the AI adapt and align better with users’ preferences on future tasks, improving its impact on productivity.
How it works
Explained Through A Task Request
Prompts For Tasks
As your AI partner, chat or speak with Atlas as you would a colleague at work simply by conversing. Our agents can autonomously handle scheduling and research tasks in our Beta lunch, with more skills coming soon.
A few sample skills include:
Schedule Meetings: Our AI agents will find the right time across time zones for internal and external meetings; for users outside your company, agents can initiate email discussions to find a time.
Calendar Analysis: Our AI agents can provide suggestions for managing your time better and to make you more productive, and then make these calendar changes for you.
Conduct Research: Get insights to your most pressing questions from search engines and multiple generative AI models.
Chat and Get Advice: Ask our AI agents for advice, a great joke or to write a poem. Choose from different agent personalities that match your preferences and role type.
Atlas is a multi-agent system specialized in admin tasks. These agents work together to solve complex problems through a multi-step work plan.
Specialized Functions: Each AI agent focuses on a specific task, such as scheduling, research, reservations, outreach, travel booking, and much more.
Collaborative Workflow: Our AI agents collaborate to ensure efficient task completion through a common work plan that is built uniquely to solve each problem or prompt.
Unified User Interface: All tasks are done within one cohesive interface. Whether an AI agent is booking a meeting or conducting research (or in the near future, making reservations, conducting outreach or booking travel), it stays in our UI through the lifecycle of a task.
Scalability and Flexibility: Our AI agents adapt and scale to handle a range of complex ‘chained’ tasks.
Tree Of Thoughts
Atlas utilizes a "Tree of Thoughts" model in its multi-agent system to efficiently process and respond to user queries, with specialized agents collaborating on various aspects of the task.
Input Analysis: The selected AI agents breaks down queries using natural language processing.
Tree of Thoughts Formation: The AI agent constructs a logic tree with each branch representing a potential combination of solutions. Over time, these trees become more efficient through reinforcement learning.
Agent Collaboration: Our AI agents work on branches simultaneously, pooling expertise to simulate and test in a safe environment.
Comprehensive Output: Our AI agents combine insights from all skills to provide a well-rounded response.
When Atlas's chosen AI agent successfully completes a task, it replicates the solution and stores it in a simulation environment for future problems.
Recording Success: Our AI agents identify the strategies and decisions that led to success and store them in a bank of inference.
Learning from Experience: The stored strategies and decisions are analyzed by machine learning algorithms using anonymized data to find commonalities with other successful strategies.
Improving Future Performance: Through this analysis, our AI agents optimize their approach, leading to quicker and more accurate task scenarios. This means faster and more accurate handling of tasks over time and a faster learning curve for new skills.
Adapting to User Preferences: As the agents learn from each task, they become more personalized, adapting to the users’ specific needs. Put simply, they become better at getting tasks done more efficiently.
Atlas's agents efficiently schedule the requested meetings and notify users, then use the success to improve future task handling.
Task Completion: Successfully schedules meetings and informs the users.
Data Storage: Records the successful process in its training environment.
Learning and Optimization: Analyzes the success to refine algorithms.
Enhanced Future Performance: Applies improvements for quicker, more advanced personal AI tasks.