Multi-Agent Systems
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Plano-Orchestrator: Fast Open Source LLMs for Multi-Agent Systems
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Plano-Orchestrator is a new family of open-source large language models (LLMs) designed for rapid multi-agent orchestration, developed by the Katanemo research team. These models prioritize privacy, speed, and performance, enabling them to efficiently determine which agents should handle user requests and in what order, acting as a supervisory agent in complex multi-agent systems. Suitable for various domains, including general chat, coding tasks, and extensive multi-turn conversations, Plano-Orchestrator is optimized for low-latency production environments. This innovation aims to enhance the real-world performance and efficiency of multi-agent systems, offering a valuable tool for developers focused on integrating diverse agent functionalities.
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Scalable AI Agents with NeMo, Bedrock, and Strands
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AI's future lies in autonomous agents that can reason, plan, and execute tasks across complex systems, necessitating a shift from prototypes to scalable, secure production-ready agents. Developers face challenges in performance optimization, resource scaling, and security when transitioning to production, often juggling multiple tools. The combination of Strands Agents, Amazon Bedrock AgentCore, and NVIDIA NeMo Agent Toolkit offers a comprehensive solution for designing, orchestrating, and scaling sophisticated multi-agent systems. These tools enable developers to build, evaluate, optimize, and deploy AI agents with integrated observability, agent evaluation, and performance optimization on AWS, providing a streamlined workflow from development to deployment. This matters because it bridges the gap between development and production, enabling more efficient and secure deployment of AI agents in enterprise environments.
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Plano-Orchestrator: Fast Multi-Agent Orchestration
Read Full Article: Plano-Orchestrator: Fast Multi-Agent Orchestration
Plano-Orchestrator is a newly launched family of large language models (LLMs) designed for fast and efficient multi-agent orchestration, developed by the Katanemo research team. It acts as a supervisory agent, determining which agents should handle a user request and in what order, making it ideal for multi-domain scenarios such as general chat, coding tasks, and extended conversations. This system is optimized for low-latency production deployments, ensuring safe and efficient delivery of agent tasks while enhancing real-world performance. Integrated into Plano, a models-native proxy and dataplane for agents, it aims to improve the "glue work" often needed in multi-agent systems.