Google's ADK for agentic AI development – and some general thoughts

On Tuesday, June 3rd, 2025, I spent the day at Google's Cambridge, MA site at their "Build with AI" event. It was a hands-on tutorial to make agentic AI systems using Google's technology. The event crystallized a few things for me and helped sharpen my thinking.

In this blog post, I'm going to review the workshop and talk about my general thoughts

(Kenneth C. Zirkel, CC BY 4.0 <https://creativecommons.org/licenses/by/4.0>, via Wikimedia Commons)

The goal of the workshop was to build a working agentic AI system using MCP, A2A, and the Google Agent Development Kit (ADK). Of course, this was all done using GCP.

The session started with an overview and some theory. Thankfully, this was done well; the team kept the introductions short and dived straight into the workshop. The theory was standard stuff, an introduction to the technologies used and some of the relevant history.

Something like 70% of the workshop was setting up various Google services, for example, a web server to serve the app, a server to serve the backend, and so on. Thankfully, this was all script based, but there were a lot of scripts. This really brought home to me the role of DevOps in AI. Someone asked about AI ops, and I agree with the question, it all felt like an outgrowth of DevOps.

The Python code we did use was pretty simple. Frankly, it was just a few API calls. The focus was on the API call arguments, making sure we had the right arguments in place for what we were trying to do. I'm going to go as far as saying that the Python coding piece was trivial; there was nothing that would cause problems even for an entry-level programmer. It was made even easier by being cut and paste, we didn't even have to figure out the right arguments.

The presenter was keen to point out the message passing between servers and how we could debug it through the Google environment. This was my concern. I've tried to debug message passing between independent systems before, and it wasn't a good experience. Having Google provide a "trace" is very helpful and reduces my concerns quite a bit.

The workshop took about four hours and I managed to build the complete system a little while before the end.

Overall, I enjoyed it and got a lot out of it. Could I build their demo system from scratch by myself? No. The reason is, all the setup that needs to be done with the various servers. It's not at all clear to me the why behind some of the config scripts. But note the problem is not a data science one, or a software one, it's a DevOps problem. Do I feel I understand A2A and MCP better? Yes.Do I recommend it? Yes.

The workshop is called "Build with AI" and it's going on the road soon.

Agentic systems are not the preserve of data scientists any more. In fact, it's hard to understand what benefits a data scientist would bring to the table.

Over the last year, the development of various abstractions, for example, A2A, MCP, LangChain, etc. have made it much easier to build AI systems. We've got to the stage where these things are pretty much "off-the-shelf" APIs. With one glaring exception, AI, and agentic AI in particular, now looks like a software engineering problem, so it feels like the preserve of software engineers.

Because frameworks like MCP and A2A are all about inter-system communication, message passing is now key. Frameworks all use some form of JSON message passing underneath. This make debugging much harder and means we need to see what messages have been passed between systems. To their credit, Google knows this and has produced software to let you trace messages. dDebugging message passing is still new to many software engineers and I expect some problems, even with Google's tools.

AI systems are all about calls from one system to another. This obviously means permissioning, but it also means cost. A poor set up can cost a company a great deal of money and/or give a poor user experience. These kinds of problems are usually associated with DevOps. In fact, my overall impression of the Google workshop was it was mostly DevOps with some basic coding thrown in.

In mid-2025, what skills do you need to develop agentic AI systems?