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Google: Agents – Architecture, Tools, and Applications

Jeremy WeaverJanuary 6, 2025
Premium

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 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.

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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.

Jeremy WeaverApril 4, 2025

Healthcare AI Agents Need a Unified Patient Ontology

Self-hosted AI agents for healthcare break when patient data is scattered across EHR, scheduling, claims, and lab systems. The prerequisite is an ontology — a governed patient data layer the health system owns and runs itself — that unifies those silos before any agent is deployed.

Miguel AmigotJune 23, 2026

Financial Services AI: Unify Data Silos With an Ontology

Self-hosted AI for financial services breaks when customer data is scattered across core banking, CRM, risk, and KYC/AML systems. The prerequisite is an ontology — a governed knowledge graph the institution owns and runs itself — that unifies those silos before any agent is deployed.

Miguel AmigotJune 23, 2026

Sovereign AI for Government Starts With a Data Ontology

Sovereign AI for government agencies fails when constituent data is scattered across case management, benefits, permitting, and records systems. The prerequisite is an ontology — a governed knowledge graph the agency owns and runs itself — that unifies those silos before any agent is deployed.

Miguel AmigotJune 23, 2026

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