Symphony - Building Intelligent Agent Ecosystems

01. APPROACH

Hierarchical planning for autonomous agent systems

Symphony introduces a revolutionary approach where AI systems can dynamically create specialized sub-agents tailored to specific tasks within a larger mission.

Unlike traditional LLMs that generate responses in one continuous stream, our hierarchical approach enables sophisticated planning, task decomposition, and specialized execution.

Core Principles

Our approach is built on the principle that complex tasks require specialized expertise, and that a coordinated team of specialized agents can outperform a single general-purpose agent.

Orchestrator Intelligence

A central intelligence that analyzes complex tasks, designs a team of specialized sub-agents, and coordinates their execution.

Dynamic Agent Creation

Generates specific agents needed for each unique task rather than relying on a static set of predefined agents.

Self-Prompting Architecture

Sub-agents create their own system prompts based on their role, maximizing effectiveness while maintaining generalizability.

Coordinated Execution

Sub-agents execute their specific tasks in a carefully orchestrated sequence to achieve complex overall goals.

02. ARCHITECTURE

Building intelligence that creates intelligence

Orchestrator Layer

The orchestrator agent analyzes complex tasks, creates a team of specialized sub-agents, and coordinates their execution through a sophisticated planning system.

Agent Creation System

Each sub-agent receives a task description from the orchestrator and generates its own system prompt, optimizing itself for its specific role within the larger task.

Execution Framework

The orchestrator manages information flow between agents, ensuring each has the context it needs to perform effectively while maintaining a coherent approach to the overall task.

Technical Implementation

Our architecture is built on a foundation of large language models with specialized prompting techniques and a robust coordination layer that manages the flow of information between agents.

Unlike traditional AI approaches, Symphony doesn't just solve problems—it designs specialized AIs to solve them better. This creates a multiplier effect where the system's capabilities grow exponentially with each new domain it tackles.

Evaluation

Our current research shows that while specialized systems still outperform generalized ones for specific tasks, the orchestration layer excels at task decomposition, effectively breaking down complex problems into manageable components.

System Layers

Planning Layer

Handles high-level strategy, breaking down complex tasks into logical sequences of subtasks that can be assigned to specialized agents.

Agent Factory

Generates custom agents with tailored system prompts optimized for specific domains and responsibilities within the task ecosystem.

Communication Bus

Manages the exchange of information between agents, ensuring efficient collaboration and integration of intermediate outputs.

03. RESEARCH

Advancing the frontier of multi-agent systems

Our research builds on recent advances in chain-of-thought prompting, multi-branch planning structures, and domain-specific agent specialization to create a system that can tackle increasingly complex tasks.

Current Research Findings

Our team has found that the concept of constructing a "team" of subagents is highly effective at encouraging LLMs to effectively decompose tasks, which has been an active area of research.

The orchestrator consistently plans sets of sub-agents similar to those designed by humans, demonstrating its ability to identify appropriate specialists for complex tasks.

Comparative Analysis

When comparing human-planned versus AI-planned agent systems:

Human Planned	AI Planned
Research Agent	Research Analyst
Writing Agent	Content Writer
	Editor
	Citation Specialist

AI-planned systems tend to include more specialized agents, creating both opportunities and challenges for system optimization.

Research Areas

Planning Efficiency

Investigating methods to optimize the hierarchical planning process and improve the quality of agent composition decisions.

Agent Specialization

Researching techniques for creating more effective domain-specific agents through improved system prompt engineering.

Orchestration Protocols

Developing better frameworks for inter-agent communication and coordination to minimize information loss between stages.

04. EXAMPLES

Real-world agent teams designed by Symphony

Symphony excels at creating specialized agent teams tailored to specific tasks. Below are examples of agent compositions automatically generated by our system for various real-world scenarios.

Sports Management

Prompt: "Start a new professional soccer team in the Bay Area, California"

Team Manager

Oversees the overall operation of the soccer team, including team management, coordination, and strategic planning.

Recruitment Specialist

Skilled in athlete recruitment, responsible for scouting, analyzing player talent, and negotiating contracts with players.

Marketing Expert

Focused on creating awareness and generating interest for the new soccer team, including branding, promotional strategies, and fan engagement.

Financial Planner

Manages the team's budget, funding, financial projections, and sponsorship acquisition.

Stadium Operations Coordinator

Responsible for securing and managing the venue for the soccer team's games, ensuring compliance with local regulations and fulfillment of logistical requirements.

Executive Briefing

Prompt: "Brief the President of the United States on the current state of the economy"

Economic Analyst

Specializes in gathering and analyzing economic data, trends, and forecasts to compile a comprehensive overview of the current state of the economy.

Policy Advisor

Focuses on interpreting economic information in the context of existing policies, providing insights on how current economic conditions affect policy decisions.

Speech Writer

Crafts concise and impactful presentations, synthesizing information into a brief tailored for the President.

Academic Research

Prompt: "Write a paper about facial recognition technology in airports"

Research Analyst

Proficient in gathering and analyzing information from various sources, focusing on collecting data related to facial recognition technology in airport environments.

Writer

Skilled at crafting coherent and persuasive text, taking gathered research and insights to write a well-structured paper.

Tech Expert

Provides in-depth knowledge of technology, particularly in computer vision and machine learning, with insights into how facial recognition systems work.

Editor

Reviews the written paper for clarity, grammar, and coherence, ensuring the final document is polished and well-presented.

Startup Development

Prompt: "You're a startup founder charged with creating a brand new kind of beef jerky"

Product Developer

Formulates and develops new beef jerky recipes that are healthy and flavorful, ensuring quality ingredients and nutrition standards.

Marketing Specialist

Creates marketing strategies, branding, and promotional campaigns to effectively position the beef jerky product in the market.

Supply Chain Manager

Oversees procurement of raw materials, inventory management, and logistics to ensure consistent supply of ingredients and efficient distribution.

Sales Agent

Builds relationships with retailers, wholesalers, and distributors to sell the beef jerky and expand its market reach.

Nutritionist

Provides expertise on health benefits and nutritional content of ingredients to ensure the beef jerky aligns with health trends and dietary needs.

05. FUTURE

Towards general intelligence through agent composition

Vision & Roadmap

The ultimate goal of this research is to develop a system that, given any prompt, can create a plan, design the sub-agents it needs, and execute complex tasks as if it were created specifically for that purpose—a significant step toward Artificial General Intelligence.

Agent Communication

Implementing direct agent-to-agent handoffs to reduce information loss and enable more sophisticated collaboration.

Structured Data Exchange

Moving from natural language to structured data formats like JSON for more efficient and reliable communication.

Iterative Refinement

Implementing SelfRefine-style approaches to improve agent system prompts through multiple iterations.

Meta-Learning

Enabling the system to learn which agent compositions work best for different types of tasks over time.

Development Roadmap

Phase 1: Foundation

Establishing core orchestration protocols and initial agent creation frameworks for basic task decomposition.

Phase 2: Specialization

Developing more sophisticated domain-specific agents with enhanced capabilities for complex problem-solving.

Phase 3: Integration

Creating seamless workflows between agents with advanced communication and memory sharing mechanisms.

Agents creating agents