A-Team
“ In 2022, an elite ensemble of LLM agents was created by leading AI researchers, transcending expectations. Quickly surpassing their original purpose, these models ignited the creative minds of innovators everywhere. Today, they join forces to tackle the world's toughest challenges. If you're currently faced with a problem and desperate for a solution… you might need the prowess of an A-Team.”
Autonomous Agent Teams
April, 2023
A proposed approach for improving the efficacy of autonomous agents through – human-agent collaboration, specialized team members, dynamically coordinated work, team work expectations, inter-agent feedback and work refinement .
As humans, we excel at complex and varied tasks by developing specialized skills, collaborating across disciplines, setting and meeting expectations, and utilizing constructive feedback loops for improvement. These systems and conventions help both individuals and teams focus, coordinate, and enhance their efforts to achieve remarkable results.
Advanced LLMs like GPT-4 already display impressive capabilities, but how might we harness the simple, yet powerful strategies of human collaboration to optimize their performance further?
Establishing the right ‘team’ with clear goals, success criteria, and autonomy fosters an environment in which LLMs can likely excel. By translating larger objectives into their necessary work outputs and subdividing these into specialized work streams we can promote collaboration, streamline performance, and drive high-quality results.
This approach, like those before it, is likely an interim step as we explore the true nature of these tools – determining which aspects they truly need to work well vs. those that help us humans understand and work with them better.
The Brief History of LLM Tools
01QA | Ask a question, get an answer |
02Chat | Build upon answers with memory |
03Agent | Provide LLM a task and tools |
04Autonomous Agent | Allow agents to determine and act on tasks for a goal |
05Multiple Autonomous Agents | Multiple, specialized autonomous agents working in parallel |
06Autonomous Agent Teams | Multiple, specialized autonomous agents working in parallel + work coordination & collaboration, work expectations, inter-agent feedback & refinement |
Core Concepts
Human-Agent Collaboration
- Seamless cooperation between AI and humans, with Human Director functioning as an adept CEO
- Human director sets objectives, success criteria, and monitors progress, providing guidance when needed
Specialized, Self-Improving Team Members
- LLMs Team Members are focused on specific roles to generate and review work, ensuring expertise
- Team members can persist and evolve, learning from experience to enhance their capabilities
Dynamically Coordinated Work
- LLM Team Manager breaks down projects and develops detailed work plans with success criteria
- Team Manager 'recruits' specialized Team Members and assigns them to work streams, optimizing as needed
- Team Manager regularly refines project work streams and team composition per work observations
- Team Manager reports progress and escalates issues to Human Director
Team & Individual Work Expectations
- Team Members' identities define their work input and output expectations
- Team Members' input expectations drive external work reviews and feedback
- Team and project goals guide Team Members' output expectations, driving internal work reviews
Inter-Agent Feedback & Work Refinement
- Team members review incoming work, provide feedback, ensuring work meets criteria before proceeding
- Team members' specific roles drive tailored feedback based on expertise
- Work Controllers manage distribution and consolidation, enabling effective coordination within the team
- Team members escalate intricate issues to Team Manager for intervention
System Design
LLM Agents: Roles & Abilities
LLM agents work in various roles, such as Team Managers, Team Members, and Work Controllers, contributing their specialized skills to achieve project goals. They communicate with other Team Members by exchanging inputs, outputs, and feedback, adapting their goals and success criteria per the team's larger context and constraints.
Team Structure & Elements
| Agency | The overarching entity for similar or disparate projects with a single Human Director. Allows you to scope team members and resources. | |||
| ↪ | Human Director | Responsible for setting project objectives, approving work plans, and providing guidance when needed. | ||
| ↪ | LLM Team Manager | 1 LLM agent per Agency that helps Human Director shape projects, develop success criteria and work plans, 'recruits' specialized team members (assigns or designs), and supervises/adjusts project execution. | ||
| ↪ | LLM Team Members | 1 or more LLM agents within a Project Work Stream. Focused for a specific role, skill set, experience, and perspective. Collaborates with other members, tracks learnings self-observed from feedback and refinements, and evolves to improve work quality. Likely Team Member types –
Upon initial 'recruitment' (i.e. creation by Team Manager for a specific project), a Team Member can optionally persist within the Agency, acquiring new experiences and learnings to improve their work, and be assigned to future Projects. Instances of Team Members can be assigned to one or more concurrent Projects and optionally develop and utilize shared learnings. | ||
| ↪ | Tools | Cross-project, saved tools (e.g., web search, APIs) that support the development of Team Members and their work. | ||
| ↪ | Projects | 1 or more per Agency. A distinct work initiative with specific goals, success criteria, and clear scope that is defined between the Human Director and the Team Manager. Drives coordination and collaboration between Team Members and the Team Manager. | ||
| ↪ | Project Tools & Resources | Assigned saved tools and/or project-specific tools and resources (e.g. external databases) defined to meet the needs of the project. | ||
| ↪ | Work Streams | 1 or more per Project that are created as needed to produce the work outputs that satisfy the project's requirements. Assembles Team Members into efficiently coordinated sequences to produce necessary work outputs. | ||
| ↪ | Assigned LLM Team Members | Instance(s) of Team Members assigned to a specific Project Work Stream. | ||
| ↪ | LLM Work Controllers | 0 or more LLM agents within a Work Stream that provide work distribution and consolidation when needed between Team Members. Verifies and divides/merges work from contributing member(s) to receiving member(s) in Work Stream. | ||
Project Creation & Flow
A project begins with the Human Director defining objectives and collaborating with the LLM Team Manager to identify required work outputs and success criteria. The LLM Team Manager decomposes required work outputs into necessary Work Streams, assigning or creating specialized Team Members to fulfill distinct roles within those Work Streams. The Team Manager conducts ongoing monitoring and adaptation through iterative adjustments to address novel challenges when they arise, and escalates issues to the Human Director when necessary.
Work Stream Processes
- Work Completion: Team Members perform tasks according to their roles, creating and refining outputs to match team and individual expectations.
- Feedback Loops & Work Refinement: Team Members iteratively refine their work based on received feedback from other Members with work dependencies, addressing concerns, and optimizing results.
- Work Coordination: As work outputs flow between Team Members, work is verified and divided or merged as needed to handle on-to-many or many-to-one member workflows, ensuring smooth collaboration and accurate results.
- Escalation Mechanisms: LLMs can escalate complex issues to the Team Facilitator, which in turn is escalated to Team Manager and Human Director for guidance and intervention as needed.
Team Member Elements
Team Member Memory
Team Member's experience (their observations and learnings from work feedback and refinement) can optionally be persisted beyond a project –
- Personal Memory: Individual memory for team members, tailored to create learning experiences and improve skills over time; enables faster adaptation to tasks and helps track personal performance improvements.
- Shared Memory: Centralized knowledge storage accessible by teams and team members based on predefined access controls; facilitates collaboration and resource sharing while accommodating compartmentalization when needed.
- Isolated Work Environments: Temporary isolation for teams or team members, allowing them to work with their own memories without affecting or being affected by others; facilitating controlled experimentation and isolation when necessary.
Practical Use Cases
A-Teams can tackle complex and dynamic challenges across various domains working together in specialized teams, collaborating across disciplines, and adapting to complex scenarios to help humans tackle demanding projects and achieve remarkable results. Example applications –
- Product Development :
An A-Team is formed to design, develop, and launch new software products, streamlining market research, feature design, UX/UI, programming, proxy-user testing, quality assurance, and marketing strategy to drive success.
- Disaster Relief Efforts :
An A-Team optimizes disaster relief coordination after natural catastrophes by analyzing real-time data, planning logistics, allocating resources, identifying vulnerable communities, and liaising with local authorities and NGOs.
- Scientific Research :
An A-Team helps advance specific scientific research in fields like climate change or renewable energy, specializing in data analysis, experimental design, literature review, report drafting, and fostering global collaboration among researchers.
- Crisis Management :
An A-Team boosts decision-making during business crises like cybersecurity breaches or stock fluctuations, aiding human teams by gathering information, assessing risks, formulating strategies, and processing crisis communication.
- Smart City Planning :
An A-Team is formed to enhance urban design, traffic management, and infrastructure development for a sustainable city by analyzing real-time data, proposing innovative solutions, and partnering with urban planners and local authorities.
- Education :
An A-Team complements human teachers by analyzing student performance, identifying learning gaps, tailoring experiences, assisting in curriculum design, personalizing content, and offering virtual mentorship to maximize learning outcomes.
Related Ideas & Work
In a quickly developing field, like practical AI, many minds stumble onto similar ideas at the same moment, building upon what came just before. I'm certain others are working on approaches similar to these ideas in their own ways right now, which makes it all the more exciting.
Similar ideas and current work I'm aware of –
- 🚫
AA Team ? = so, it turns out someone has already nailed down the AA moniker pretty well… - 🚫
AI Team ? = well sure, but it's pretty on the nose and should probably be reserved for your science department - ✅
A Team ? = invokes 1980's savvy and highly trained group of team mates working together to achieve nearly unsolvable goals