There is a wide gap between an agent that demos well and an agent you can put in front of customers. The demo needs a clever prompt. The production system needs reliability, governance, memory, scaling and a way to debug it when it misbehaves at 2am. Google's answer to that second problem is a two-part stack: the Agent Development Kit for building, and Vertex AI for running.
This is the serious end of the Google AI stack — for engineers and enterprises, not weekend prototypes. Here is how it fits.
ADK: the framework you build in
The Agent Development Kit (ADK) is Google's open-source, code-first framework for building, debugging and deploying agents. It is available in Python, Go, Java and TypeScript, and it was designed for multi-agent systems from the start — you compose teams of specialised agents that collaborate and delegate tasks, rather than cramming everything into one over-loaded prompt. Adoption tells the story: Google's Python ADK has been downloaded more than seven million times.
Code-first matters here. For anything you intend to maintain, the precise control ADK gives you over agent behaviour, tool use and orchestration beats a purely visual builder — the same reason production teams write code rather than click through wizards.
Vertex AI: the platform you run on
If ADK is the toolkit, Vertex AI Agent Builder is the factory floor. Now folded into the rebranded Gemini Enterprise Agent Platform (announced at Cloud Next 2026), it is where agents get deployed, scaled and governed. It bundles several pieces: ADK for code-first building, Agent Studio as a low-code visual canvas, Agent Garden for prebuilt templates, Model Garden for model access, and Agent Engine — the managed runtime that handles deployment, scaling, sessions and memory so you are not rebuilding that plumbing yourself.
Model Garden: not locked to Gemini
One detail that surprises people: a Google-hosted agent does not have to run on Gemini. Through Model Garden you can call 200+ foundation models, and as of Cloud Next 2026 Anthropic's Claude models — Opus, Sonnet and Haiku — are first-class options alongside Google's Gemini and open Gemma models. That freedom matters, because choosing the right model per task is half of building an agent that is both reliable and affordable.
Where ADK sits among the alternatives
ADK is Google's entry in a crowded field of agent frameworks. It leans on open standards — support for tool protocols and agent-to-agent communication — which keeps it interoperable rather than walled off. As with the coding tools, the durable skill is not the framework's syntax; it is understanding how to design, orchestrate and evaluate multi-agent systems. That knowledge transfers across ADK and its rivals, which is how we teach it in agentic orchestration.
When to reach for this
Do not start here. For a quick internal tool, Opal or AI Studio is faster; for building software interactively, Antigravity is the home. Reach for ADK and Vertex AI when an agent has to be reliable, governed and run at scale — a customer-facing assistant, an internal system thousands of staff depend on, anything where "it worked in the demo" is not good enough. The tell is when governance and uptime start to matter more than speed of prototyping.
Building the capability
Production agents are an engineering discipline, not a prompt trick — design, tool use, evaluation, orchestration and governance. AITraining2U's AI Engineering and Mastering Claude & Multi-Agent Orchestration programmes teach that discipline in a way that transfers across ADK, Vertex AI and other stacks, and both are HRD Corp SBL-KHAS claimable for eligible Malaysian employers — see the HRDC guide. For the wider context, start with Google's AI productivity stack and our primer on what agentic AI is.
