Limitations and Challenges for Agents solved by Leea

The Leea Protocol significantly enhances the functionality of AI agents by addressing critical limitations:

• Scalability and Cost: The deployment and scaling of AI agents on traditional cloud infrastructure can be cost-prohibitive. The Leea Protocol addresses this issue by leveraging decentralized computing, thereby reducing operational expenses and enhancing accessibility.

• Corporate-Level Security: Traditional corporate-level security requirements often necessitate on-premises deployment of Multi Agent Systems, involving significant investment in dedicated servers and ongoing maintenance. With Leea, institution-grade security can be achieved without the need for local deployment. All virtual machines (VMs) within Leea are secured through a double encryption mechanism: the segment of RAM used by the VM for storing MAS data is encrypted using AMD SEV (Secure Encrypted Virtualization) and Intel TDX (Trust Domain Extensions) technologies. Additionally, the VM image and storage are encrypted with a private key stored in a decentralized API. This key is created and distributed to the end-user in a segmented manner. Distributed key generation (DKG) via multiple nodes ensures that no single entity holds the entire key. This approach guarantees that the virtual machines operating the multi-agent systems are secure and capable of executing security-intensive tasks without reliance on traditional cloud or self-hosted infrastructure, providing both robustness and scalability.

• Deployment for Multi-Agentic Systems: multiple specialized agents can be easily deployed in different cloud and premises allowing easy migration without the need to:

  • rewriting the deployment code

  • configuring networking

  • setting up security rules

  • managing DNS records

  • reconfiguring inter-agent communication

• Specialization vs. Generalization: A single large language model tasked with multiple functions often performs worse than specialized models focused on specific tasks. For instance, using smaller, specialized models for specific roles (e.g., writing emails, generating code) can lead to more efficient outcomes compared to using a generalized model. Leea's infrastructure can facilitate the deployment of specialized agents built on top of different libraries and frameworks, allowing for effective delegation and coordination across different models, thereby enhancing overall system performance. With the Leea SDK, developers can connect frameworks like CrewAI, AutoGen, and components from the LangChain ecosystem, including RAG systems, Chroma, and Pinecone vector databases. The SDK provides streamlined tools and APIs to simplify the process of integrating multi-agent systems across the entire technology stack, supporting seamless communication, task delegation, and enhanced security across agents.

• Every agent where local or cloud based needs to be discovered. Leea Service Discovery (LSD) handles public registry of Agent Name Service (ANS) for it to become censor resistant and agnostic.