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An application that tries to achieve a goal by observing the world and acting upon it using the tools it has at its disposal.
A gen AI Agent
An iterative process where the agent observes, interprets, reasons, and acts, often using prompt engineering.
Reasoning Loop
Functionalities that allow the agent to interact with its environment, such as accessing and processing data or interacting with hardware.
Tools
The brains of the AI system, which consist of various algorithms that learn patterns from data and can make predictions or generate new content.
Model
A type of agent that is built with predefined paths and actions.
Deterministic Agent
A type of agent that is defined with natural language using LLMs to give a real conversational feel to your chatbot.
Generative
A type of agent that combines both deterministic and generative capabilities, and this combination makes them very powerful.
Hybrid Agents
The foundation for building and scaling AI initiatives.
Platform
Google Cloud’s unified machine learning platform designed to streamline the entire ML workflow. It provides the infrastructure, tools, and pre-trained models you need to build, deploy, and manage your ML and generative AI solutions.
Agent Platform
Google's model option that allows you to pick from existing Google, third-party, or open-source models.
Model Garden
Google’s model option that trains and uses your own models. Go fully custom and create and train models at scale using its ML framework. Or use Agent Platform AutoML to create and train models with minimal technical knowledge and effort.
Model Builder
It provides the core computing resources needed for generative AI. This includes the physical hardware (like servers, GPUs, and TPUs), along with the essential software needed to train, store, and run AI models.
Infrastructure
You can run AI solutions on infrastructure (devices or servers) closer to where the action is happening.
AI on the Edge
Google's most efficient and compact AI model, specifically designed to run on devices.
Gemini Nano