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The "Google AI Agents" whitepaper introduces the concept of AI agents, which enhance generative AI models with reasoning, logic, and access to external information. Agents use a cognitive architecture comprising a language model, tools for interacting with the outside world, and an orchestration layer for managing decision-making. The document distinguishes between extensions, functions, and data stores as key tool types, outlining their specific purposes and implementation methods. It also explores techniques for improving model performance through targeted learning approaches like in-context learning, retrieval-based learning, and fine-tuning. Furthermore, the whitepaper provides a practical demonstration using LangChain and highlights Google's Vertex AI platform for building production-ready AI agents.
The "Google AI Agents" whitepaper introduces the concept of AI agents, which enhance generative AI models with reasoning, logic, and access to external information. Agents use a cognitive architecture comprising a language model, tools for interacting with the outside world, and an orchestration layer for managing decision-making. The document distinguishes between extensions, functions, and data stores as key tool types, outlining their specific purposes and implementation methods. It also explores techniques for improving model performance through targeted learning approaches like in-context learning, retrieval-based learning, and fine-tuning. Furthermore, the whitepaper provides a practical demonstration using LangChain and highlights Google's Vertex AI platform for building production-ready AI agents.