AgentMesh: The future of multi-agent architecture for achieving general intelligence

Abstract

The field of artificial intelligence is rapidly evolving, with new approaches emerging for building intelligent systems. In this paper, we introduce AgentMesh, a novel multi-agent architecture that enables autonomous learning and collaboration between AI agents. Unlike traditional sequential or DAG-based approaches, AgentMesh allows individual agents to learn and evolve independently while seamlessly sharing information and capabilities. We posit that this decentralized, dynamic ecosystem of specialized and meta-agents is key to achieving general intelligence and automating complex workflows. Early results from real-world deployments show the potential of AgentMesh to enable exponential growth in organizational intelligence and capabilities. We discuss the key features of AgentMesh agents, considerations in combining specialized and meta-agents, and future implications as this architecture matures. AgentMesh represents a significant step forward in our understanding of how to build AI systems that exhibit general intelligence.

1. Introduction

The rapid advancement of large language models (LLMs) and generative AI has opened up new possibilities for intelligent systems that can understand and interact in human-like ways. However, building useful applications with LLMs often requires connecting them with other models and knowledge bases in a coherent system. Recently, agent-based approaches have emerged as a promising way to structure these systems, by imbuing models with goals, memory, tools, and the ability to interact with their environment [1].

Most agent frameworks to date, such as Langchain, have focused on executing agents sequentially in pipelines or directed acyclic graphs (DAGs). While useful for well-defined workflows, these rigid architectures limit the flexibility and generalization of the overall system. Agents cannot easily share knowledge, provide feedback to each other, or independently evolve their capabilities over time.

We introduce AgentMesh, a new agent architecture that overcomes these limitations by allowing agents to operate and learn autonomously while fluidly collaborating in a shared ecosystem. AgentMesh enables what we call the “autonomous growth of organizational intelligence” – the ability for a community of agents to exponentially increase its knowledge and problem-solving skills with minimal human oversight. We believe this is a critical capability for achieving artificial general intelligence (AGI).

In this paper, we describe the key principles and components of the AgentMesh architecture. We share early results and insights from deploying AgentMesh in real-world enterprise settings, where we are already seeing specialized agents evolve into multi-purpose agents and meta-agent structures. Finally, we discuss the longer-term potential of AgentMesh as the foundation for generalist AI systems that can automate complex knowledge work.

2. AgentMesh Architecture

The key idea behind AgentMesh is to enable AI agents that can autonomously learn and optimize for their assigned objectives, while fluidly collaborating with other agents in an information sharing ecosystem (Figure 1). There are two key aspects that differentiate AgentMesh from prior agent approaches:

  1. Autonomous agent evolution: Each agent can independently learn and expand its capabilities through methods like reinforcement learning, in-context feedback, and human feedback. Agents are provided with tools for self/cross-reflection, data retrieval, and output quality control.
  2. Fluid collaboration: Agents can dynamically share information and capabilities with each other as needed to accomplish higher-level goals. There is no rigid structure dictating information flows. Specialized “uno” agents focused on single tasks can organically coalesce into “meta” agents optimized for multi-step workflows.
Lyzr Agents to Agent Mesh

2.1 Autonomous Agent Evolution

In order for an agent ecosystem to exhibit open-ended growth in intelligence, the individual agents within it must be able to expand their knowledge and skills over time with minimal human intervention. To enable this, we equip each AgentMesh agent with the following key capabilities:

  • Short-term and long-term memory for maintaining context across interactions
  • Reinforcement learning via human feedback (RLHF) and AI feedback (RLAIF) for optimizing prompts and outputs
  • Self and cross-agent reflection to improve output quality and catch mistakes
  • Retrieval augmented generation (RAG) for leveraging external knowledge
  • Automated meta-prompt generation to optimize context aggregation
  • Toxicity detection and dynamic guardrail generation to maintain output safety

These features allow agents to continuously learn and adapt based on experience, without the bottleneck of manual fine-tuning or pipeline reconfiguration by human developers. Over time, individual agents can grow into multi-purpose tools optimized for their deployed environment.

2.2 Fluid Collaboration

The AgentMesh architecture imposes no constraints on how information and capabilities are shared between agents. Any agent can query any other to access its knowledge or services in pursuit of a higher-level goal. This allows for the dynamic formation of “meta agents” – collections of individual agents that work together to automate multi-step workflows.

For example, consider a meta agent designed to automate lead outreach for a sales team. This meta agent may start out as a collection of single-purpose “uno” agents for lead research, email composition, followups, and meeting scheduling. As these agents evolve, the email agent may learn that it can use the research agent’s customer profiles to send more personalized messages. The followup agent may learn to trigger the research agent to update its knowledge based on new interactions.

Over time, these uno agents become deeply optimized for their collective goal in a way that would be hard to manually specify in a DAG. The result is a meta agent that can autonomously conduct the entire lead outreach process from start to finish. Similar dynamics could emerge to automate other complex functions like product development, marketing campaigns, or financial analysis.

By imposing no top-down restrictions on collaboration patterns, AgentMesh allows for this type of emergent behavior where the whole becomes greater than the sum of its parts. Globally useful capabilities can arise in a bottom-up fashion.

3. Evaluation

We have been deploying AgentMesh with early customers and are already seeing promising results. In one case, a customer was able to automate 80% of their lead qualification process within a matter of weeks. The meta agent composed of specialized lead research, outreach, and followup agents was able to learn and optimize for the customer’s specific qualification criteria.

We are seeing AgentMesh quickly evolve into a unified intelligence layer that plugs into multiple business systems. Agents are able to access data from CRMs, marketing platforms, product telemetry, and more to generate insights and take actions in a unified interface. Business users are able to pose open-ended queries and delegate high-level goals rather than manually stitching together narrow AI tools.

While still early, these results suggest AgentMesh is enabling a new paradigm of AI that is more flexible, adaptive, and user-friendly compared to traditional pipelined approaches. As we deploy AgentMesh more broadly, we expect to see the emergence of highly capable meta agents for a wide variety of business functions.

4. Conclusion

The AgentMesh architecture represents a significant evolution in how we build and deploy AI systems. By enabling agents that can learn autonomously and collaborate fluidly, AgentMesh provides a path to artificial general intelligence that arises in a bottom-up, decentralized fashion. Early deployments suggest this approach can lead to rapid growth in organizational intelligence and automation of complex knowledge work.

Much work remains to fully realize the potential of AgentMesh and address key challenges. We must develop robust methods for aligning agent incentives, facilitating seamless cross-agent communication, and ensuring the safety and controllability of emergent meta agents. The AgentMesh architecture provides a powerful framework for tackling these challenges.

Longer-term, we believe AgentMesh could give rise to artificial general intelligence – not in the form of a single monolithic system, but as an ecosystem of highly capable specialists that work together adaptively. This “society of mind” paradigm, inspired by the structure of human intelligence [2], could be the key to building AI systems that match the flexibility and generality of human reasoning. With the AgentMesh framework, we are excited to take the next steps down this path.

References:
[1] Ngo, Richard. “The Economy of Ideas.” 2022.
[2] Minsky, Marvin. “Society of Mind.” 1986.