Google: Agents – Architecture, Tools, and Applications
Generative AI agents extend language models by using external tools and orchestrated reasoning frameworks like ReAct and Chain-of-Thought, with practical implementations shown through examples such as LangChain and Vertex AI.
Google: Agents – Architecture, Tools, and Applications
Summary of Read" class="text-blue-600 hover:text-blue-800" target="_blank" rel="noopener noreferrer">https://www.kaggle.com/whitepaper-agents'>Read Full Report
This whitepaper explains Generative AI agents, programs extending the capabilities of language models. Agents achieve goals by using tools (Extensions, Functions, and Data Stores) to access external information and perform actions.
The paper details agent architecture, including the model, tools, and orchestration layer, and explores various reasoning frameworks like ReAct and Chain-of-Thought.
It also discusses methods for enhancing model performance through targeted learning and provides examples using LangChain and Vertex AI. Finally, it summarizes the key components and future directions of agent development.
Related Articles
Google: Agents Companion
The document "Agents Companion" outlines advancements in generative AI agents, detailing an architecture that goes beyond traditional language models by integrating models, tools, and orchestration. It emphasizes the importance of Agent Ops—combining DevOps and MLOps principles—with rigorous automated and human-in-the-loop evaluation metrics and showcases the benefits of multi-agent systems for handling complex tasks.
The MCP Context Window Problem: Why AI Agent Architecture Matters More Than Model Size
MCP servers are consuming up to 72% of AI agent context windows before a single user message is processed. Here is why smart agent architecture — not bigger models — is the real solution.
Amazon's AI Coding Crisis Reveals What Every Organization Needs: Controlled Agent Infrastructure
Amazon's recent production outages from AI coding agents reveal a fundamental truth: organizations need AI infrastructure they own and control. Here's what the industry can learn.
Why 1 Million Tokens of Context Changes Everything — If You Own the Infrastructure
Anthropic just made 1 million tokens of context generally available. Here's why long context only matters if the infrastructure running it belongs to you.
See the ibl.ai AI Operating System in Action
Discover how leading universities and organizations are transforming education with the ibl.ai AI Operating System. Explore real-world implementations from Harvard, MIT, Stanford, and users from 400+ institutions worldwide.
View Case StudiesGet Started with ibl.ai
Choose the plan that fits your needs and start transforming your educational experience today.