Building Multi-Agent Large Language Model (LLM) Systems Towards Compound AI

Enables readers to shift from AI assistants to self-governing AI systems by harnessing large language models (LLMs)

Building Multi-Agent Large Language Model (LLM) Systems towards Compound AI: Enabling the Transition from Assistant AI to Autonomous AI Systems offers a thorough examination of constructing multi-agent systems (MAS) that harness the unique power of large, small, vision and multimodal language models.

After reviewing the basics of agentic AI systems and recent advancements in the language model (LM) space, the book moves on to discuss:

• Production and deployment of next-generation language models in cloud environments (public, private, hybrid, and edge)
• How AI agents communicate, coordinate, collaborate, negotiate, and compete to build, deploy, run, manage and improve Agentic AI Systems
• LLM-based conversational agents, the significance of prompt engineering, and the role of LLMs in multi-agent systems
• Scalable LLMs and their implications for 6G communications, and the integration of LLMs in complex event processing and manufacturing systems
• Collaborative LLMs, mixture-of-agents methods, autonomous agents, and emerging AI architectures, applied to enterprise settings

Building Multi-Agent Systems Using Language Models for Agentic AI: Enabling the Transition from Assistant AI to Autonomous AI Systems is a cutting-edge, comprehensive resource on the subject for researchers, AI engineers and data scientists, IT professionals, and graduate and postgraduate students in related programs of study.