Autonomous Agent Teams

aka A-TEAMS

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

Step	Abilities
01: QA	Ask a question, get an answer
02: Chat	Build upon answers with memory
03: Agent	Provide LLM a task and tools
04: Autonomous Agent	Allow agents to determine and act on tasks for a goal
05: Multiple Autonomous Agents	Multiple, specialized autonomous agents working in parallel
06: Autonomous 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

1️⃣ More detail from original concept, with fun diagrams →

2️⃣ More detail on refined concept being built →

---

Code (ish)

Version 1:
Single LLM via System Message

Version 2:
Multiple LLMs + Multi-Threaded Communication