Introduction
The next generation AI foundation models will achieve reasoning and logic abilities equivalent to PhD level. And while AI doctors, AI lawyers, and AI engineers are not ready to hang out their shingles, every doctor, lawyer, and engineer will want a specialized AI partner to assist them in delivering premium service to their clients. 
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The Problem
AI agent teams partnering with professionals face poor coordination, limited adaptability, and inconsistent performance. Trust issues and integration hurdles hinder adoption. AI needs better collaboration mechanisms, adaptive learning, and robust feedback loops to improve. Enhancing communication skills and ethical decision-making is crucial. The goal is to create transparent, flexible AI agent teams that learn continuously, providing reliable assistance across various professional fields.
 
The Solution
We are developing an AI Agent Team Architecture called Syzygi (pronounced SIZ-in-jee) that mimics some features of the neural net Transformer Architecture used to train LLMs. Syzygi architecture provides power and flexibility for AI agents to synchronize their tasks on one project and train as a team over many projects. As they perform more varied tasks, they become more versatile and efficient as an organization - they learn to become a better team.
 
Syzygi - AI Agent Team Architecture
In the rapidly evolving landscape of artificial intelligence, we are witnessing a convergence of multi-agent systems and transformer-based language models. Syzygi presents a novel architecture that synergizes the strengths of specialized AI agents with the powerful mechanisms found in transformer models. It is centered around a large language model (LLM) acting as a neural transformer (brain). This approach aims to enhance collaborative problem-solving, adaptability, and scalability in AI systems.
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Core Components
The LLM acts as the brain to conduct reasoning and make decisions while the agents act as its 'arms and legs.' The agent 'arms and legs' are search engines. grammar editors, and other tools to carryout the tasks to reach the goal.

At the heart of this architecture lies a team of specialized AI agents, each designed to excel in specific tasks or domains. These agents span a wide range of capabilities, from natural language processing and computer vision to data analysis and logical reasoning. Each agent has clearly defined roles and responsibilities, not only executing tasks within their domain of expertise but also providing domain-specific knowledge to other agents and collaborating on complex tasks that span multiple domains.
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The inter-agent communication is facilitated through a robust set of protocols. These include standardized calls for direct interactions, shared memory spaces for collaborative tasks, and message passing systems for asynchronous communication. 
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Central to the architecture is a large language model, instructed and given system prompts to act as a transformer-like coordinator. This "Neural Transformer" is fine-tuned with specific instructions to emulate key transformer functionalities, with custom prompts designed to trigger attention-like mechanisms and information integration. The Neural Transformer plays a crucial role in coordinating and integrating agent outputs, acting as a central hub for information flow between agents, synthesizing outputs from multiple agents into coherent solutions, and managing task allocation and prioritization based on agent capabilities and task requirements.
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One of the key innovations in this architecture is the adaptive prompting based on task context. The central LLM dynamically generates prompts for agents based on the current task and context, and adjusts its own internal prompts to optimize coordination and integration processes. This adaptability allows the system to flexibly respond to a wide range of tasks and scenarios.
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The Task Decomposition Module is another critical component, responsible for breaking down complex problems into manageable subtasks for efficient ste-by-step processing. It utilizes hierarchical task network (HTN) planning techniques and employs semantic analysis to identify key components of a task. This module also considers dependencies and parallelization opportunities in subtask creation, ensuring optimal distribution of work across the agent team.
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Complementing the Task Decomposition Module is the Integration Module, which combines the outputs of various agents into a cohesive solution. This component utilizes advanced natural language processing to merge textual outputs, employs data fusion techniques for numerical and analytical results, and tracks contributions from different agents. A key feature of this module is its ability to resolve conflicts and inconsistencies, implementing conflict resolution algorithms to handle contradictory outputs and utilizing the central LLM to mediate and decide on conflicting information.
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Transformer-Inspired Mechanisms
The architecture incorporates several mechanisms inspired by transformer models, adapting them for agent and task management. The attention mechanism, crucial in transformer models, is reimagined for task-agent relevance scoring and dynamic agent prioritization. The system computes relevance scores between tasks and agents based on historical performance and current capabilities, utilizing embedding techniques to represent tasks and agent skills in a shared vector space. This allows for real-time adjustment of agent priorities based on task urgency and agent performance.
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Multiple feedback loops ensure continuous improvement and adaptation. These include inter-agent feedback, where agents provide performance ratings and suggestions to each other, central LLM to agent feedback for guidance and correction, and user to system feedback for real-time adjustments based on user interactions. This multi-layered feedback system creates a dynamic, self-improving ecosystem of agents and processes.
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The weight parameter system dynamically adjusts the influence of different components. It tracks comprehensive performance metrics for each agent, assigns weights to tasks based on user priorities and system goals, and dynamically adjusts the impact of each agent's output on the final solution. This system implements a learning rate to balance stability and adaptability, ensuring that the architecture can evolve without becoming unstable.
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Information Flow and Processing
The central LLM integration collects outputs from all active agents, standardizes output formats for consistent processing, and applies its attention mechanisms to focus on the most relevant outputs. It weighs agent contributions based on their performance and task relevance, ultimately synthesizing a cohesive solution that ensures consistency and coherence in the final output.
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The output refinement stage involves iterative improvement based on feedback from users, agents, and internal evaluations. Multiple refinement cycles are run to optimize the solution before the final preparation, where the solution is formatted according to user preferences and supplemented with explanations and justifications.
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Learning, Adaptation, and Scalability
A cornerstone of this architecture is its emphasis on continuous learning and adaptation. The system employs comprehensive performance evaluation mechanisms, tracking metrics for individual agents and assessing overall system performance.

Detailed logs are kept of each project complete and evauation and review comments collected. This evaluation drives the dynamic modification of system parameters, including updating agent influence based on performance and refining task-agent relevance scores.
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Prompt engineering is an ongoing process in this architecture, with continuous improvement of prompts used for the central LLM and agents. The system analyzes LLM performance to identify areas for prompt improvement and employs evolutionary algorithms to generate and test new prompt variations. Agent-specific prompts are tailored to each agent's role and capabilities, with A/B testing of different prompt structures to optimize agent performance.
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Agent skill development is another key aspect of the system's adaptability. The architecture identifies areas for agent improvement through analysis of failure cases and performance bottlenecks, and implements targeted training. 
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The architecture's scalability and flexibility are ensured through a dynamic agent pool, which allows for adding or removing agents based on task requirements. This plug-and-play architecture facilitates easy agent integration and implements criteria for agent retirement or temporary deactivation. The system balances specialization and generalization, adapting the team composition between specialist and generalist agents based on task patterns.
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To handle tasks of varying complexity, the architecture implements recursive task decomposition for highly complex problems and allows for dynamic adjustment of decomposition depth based on task difficulty. Adaptive resource allocation ensures optimal utilization of computational resources across the system.
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Finally, the architecture's multi-modal integration capabilities allow it to handle diverse types of input and output. Modality-specific preprocessing modules and unified representation schemes for multi-modal inputs enable the system to work with text, images, code, and other data types seamlessly. Specialized agents for different modalities and cross-modal translation capabilities ensure comprehensive coverage of various data types and formats.
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The transformer architecture's power lies in its ability to process sequences in parallel while capturing complex dependencies. The intricate interplay of self-attention, feed-forward networks, and normalization layers, coupled with efficient backpropagation and weight update mechanisms, allows these models to learn sophisticated patterns in data. As research progresses, we can expect further refinements in architecture design, training techniques, and optimization strategies, pushing the boundaries of what's possible with generative AI.