AI models provide the intelligence, but AI agents operationalize it. Think of an agent as a system built around a model that enables it to act, rather than just respond to a query. While a model reasons and generates intelligence (the brain), the agent plans, acts, and executes (the worker).
Vertex AI Agent Builder is a comprehensive platform designed to build, deploy, and govern production-grade agents. It grounds AI in the relevant data and integrates directly with existing business systems. To simplify the lifecycle, the platform splits the process into two distinct phases: Build and Run.
The Agent Development Kit (ADK) is a declarative framework for building agents using Python or Java. It offers developers fine-grained control over how an agent behaves, from its core reasoning logic to its safety guardrails.
With ADK, teams can define:
To speed up time-to-market, developers can use the Agent Garden, which provides prebuilt templates and reusable components.
Vertex AI Agent Engine serves as the managed runtime layer. It abstracts away the underlying infrastructure so teams can focus on reliability and scaling.
Production-Grade Infrastructure:
While models provide the "thinking," agents drive the "doing." Vertex AI Agent Builder bridges this gap by separating design (ADK) from execution (Agent Engine). This allows organizations to deploy scalable, governed AI systems that move beyond simple responses to drive actual business outcomes.
The model acts as the "brain" that reasons and generates intelligence, while the agent acts as the "worker" that plans, uses tools, and executes multi-step workflows to achieve specific goals.
The ADK is a developer framework used to build production-ready agents. It allows for fine-grained control over agent goals, instructions, reasoning logic, and tool usage through intuitive, declarative code.
It serves as the managed runtime layer that handles deployment, auto-scaling, and security compliance (such as IAM and VPC-SC), allowing teams to focus on the application rather than infrastructure.
Agents use Sessions to store user interactions and maintain conversational context, and a Memory Bank for long-term memory retrieval and personalization across different sessions.
The Agent2Agent (A2A) protocol enables interoperability between different agents, while the Model Context Protocol (MCP) allows agents to connect to an ecosystem of tools and data sources without custom integrations.
Generative AI has rapidly moved from a buzzword to a production reality. What started as experimental chatbots is now powering critical workloads, automating support, accelerating software development, and reshaping how products are built on Google Cloud.
