Google Vertex AI Agent Builder updates for support use cases: A complete overview

If you’re in the tech world, you've probably heard the term "agentic AI" floating around lately. It’s this idea that we’re moving beyond simple chatbots that just spit out answers. The next wave is AI agents that can think, plan, and actually get things done. For businesses, this is a big deal, it opens the door to automating some seriously complex work.

Leading the charge for developers is Google's Vertex AI Agent Builder, a platform designed for building these kinds of advanced, multi-agent systems. But just because it’s powerful doesn’t mean it’s the right fit for your support or IT team.

This guide will give you a straight-up look at the latest Google Vertex AI Agent Builder updates for support use cases. We'll break down what it is, what it does, and where it falls short. The goal is to help you figure out if it’s a toolkit your engineers will love, or a massive project you’re just not ready to take on.

Understanding Google Vertex AI Agent Builder

Google Vertex AI Agent Builder isn't a ready-made application you just turn on. Think of it more like a professional-grade workbench for your developers. It’s a suite of tools inside Google Cloud for building, launching, and managing AI agents that connect to your company's data and automate workflows.

Its main selling point is flexibility. It lets developers build highly custom AI experiences by using open-source frameworks like LangChain, tapping into Google’s own powerful tech (like Gemini models and BigQuery), and coordinating how different specialized agents work together.

But that flexibility comes at a cost. It’s built for teams with serious technical chops and a lot of experience in the Google Cloud world. This isn’t something your support manager can set up over a weekend. If you’re just trying to get a handle on your support tickets, you’re looking at a pretty steep learning curve.

Core components and recent updates

To really get a feel for whether Vertex AI Agent Builder is for you, you have to look under the hood. The platform is a collection of components that your team needs to assemble, not a simple switch to flip.

The building blocks: ADK, Agent Engine, and Agent Garden

The whole thing rests on three main pillars:

• Agent Development Kit (ADK): This is an open-source Python framework where your developers will spend most of their time. It’s used to write the code that dictates an agent's logic, how it "thinks," and what it does. Google mentions you can build agents in "under 100 lines of Python," which tells you right away that this is a hands-on coding job.

• Agent Engine: Once the agent’s code is written, the Agent Engine is the managed environment where it lives and runs. It takes care of the behind-the-scenes infrastructure, but your team is still on the hook for configuring, deploying, and managing the agent itself.

• Agent Garden: This is essentially a library of pre-built code samples and tools to give developers a starting point. It’s useful for inspiration, but these are just templates. They need a lot of custom work to handle your specific business rules and aprocesses.

Key 2025 updates

Google is always pushing out new features, and a few of the latest are especially interesting for support and IT folks, even if they add more complexity to the pile.

A major update is the Agent2Agent (A2A) protocol. This is a standard that lets different, specialized agents talk to each other. For a support team, you could theoretically have a "triage agent" that reads an incoming ticket and passes it off to an "order lookup agent" or a "refund agent." It's a powerful idea for building complex automation, but it also means you're not just building one agent, you're designing, coding, and managing an entire system of them.

Google has also beefed up its Retrieval-Augmented Generation (RAG), which helps agents connect to more data sources like Google Drive, Jira, and Slack to base their answers on your company’s actual knowledge. Connecting these sources is great, but it usually involves custom setup and API configurations. Now, if that sounds like a lot of work, you’re right. It’s a different approach from a tool like eesel AI, which offers one-click integrations for the same apps, letting you connect your knowledge sources and go live almost instantly, no developers needed.

This infographic shows how eesel AI simplifies knowledge integration from various sources, a key topic in the Google Vertex AI Agent Builder updates for support use cases.

Applying Google Vertex AI Agent Builder

So, how would a technical team actually use this to solve everyday support problems? Let’s walk through a couple of scenarios, keeping the required engineering effort in mind.

Building an internal IT helpdesk agent

Let’s say you want to build an agent to handle common IT requests like password resets or software access. Using Vertex AI Agent Builder, the process would look something like this:

1. Define Tasks: First, you’d map out what the agent needs to do, like figuring out if someone is asking for a password reset or a new software license.

2. Code the Logic: Your developers would then jump into the ADK and write Python code to handle the logic for each task. This means parsing what the user wants and triggering the right actions.

3. Connect Knowledge: You'd hook the agent up to your internal IT documentation, maybe sitting in Google Drive or Confluence, using the RAG engine. This step means configuring data stores and making sure the agent can pull the right information.

4. Deploy and Integrate: Finally, you’d deploy the agent to the Agent Engine and plug it into your chat tool, like Google Chat or Slack.

The whole thing is basically a mini-software project that involves coding, API work, and a lot of fine-tuning. For teams who want to solve this problem without that overhead, eesel AI does the same thing for internal support with a simple, no-code setup. You can connect it to Slack or Microsoft Teams and have it learning from your docs in minutes.

Creating a customer support agent

Alright, now let's imagine you want to build an agent for your external customers. The process would involve connecting to your helpdesk, feeding the customer support agent your public help center articles, and defining actions like escalating a ticket or checking an order status.

The big hurdle here is hooking into systems that aren't part of the Google ecosystem, like Zendesk for tickets or Shopify for order data. This requires building or setting up API connectors, which can be a huge technical lift.

This is another project that could easily stretch into weeks or months. For teams that don't have dedicated AI engineers just waiting for a project, a platform like eesel AI offers a much simpler path. With one-click helpdesk integrations, it can train on your past tickets and articles automatically, so you can be up and running in minutes.

Limitations and the reality of getting started

While Vertex AI Agent Builder is impressive, its developer-first design creates some real-world hurdles for most support and IT teams who just want a solution that works.

The steep learning curve and resource needs

Let’s be honest: Vertex AI Agent Builder is a toolkit for developers, not a tool for support managers. To build, deploy, and maintain these agents, you need someone who knows their way around Google Cloud, Python, and AI frameworks. If you don't have an engineering team ready to own this, it’s probably not going to happen.

This is a world away from the self-serve approach of eesel AI. Our platform is designed so that anyone can build, test, and launch a powerful AI agent from a simple dashboard, with zero coding required.

Vendor lock-in and ecosystem challenges

Even though Vertex AI supports open-source tools, the entire system for running your agents (the Agent Engine) lives inside Google Cloud. This can lead to serious vendor lock-in and create headaches for businesses that use multiple cloud providers or simply don't want to be tied to a single tech stack.

A better way is to use a tool that works where you work. eesel AI plugs directly into your existing tools, like Zendesk, Freshdesk, or Slack, without making you change how you operate or commit to a specific cloud.

The hidden challenge of testing

One of the biggest risks with any AI automation is unleashing it before it's truly ready. Testing complex, multi-agent workflows is incredibly hard. While Google gives developers debugging tools, there’s no easy way for a business user to see how an agent will perform on real historical data before it starts talking to your customers.

This is where eesel AI's simulation mode is a huge advantage. It lets you test your AI setup on thousands of your past support tickets in a safe environment. You can see exactly how it would have responded, get solid forecasts on resolution rates, and tweak its behavior, all before a single customer interacts with it.

This screenshot of eesel AI's simulation mode highlights a practical tool for support use cases, contrasting with the complexities of testing in Google Vertex AI Agent Builder.

Pricing for Google Vertex AI Agent Builder

One of the trickiest parts of Vertex AI Agent Builder is its pricing. It’s incredibly complex and based on components, which makes it almost impossible to predict your costs. You aren't paying a flat subscription; you're paying for how much of various cloud services you use.

The costs are split into several pieces:

• Agent Engine: You’re billed for the computing power and memory your agent uses, measured per vCPU-hour and GiB-hour.

• Model Usage: You pay for the underlying AI models, like Gemini, based on the amount of text going in and out.

• Tools and Data: You also get charged for any other Google Cloud services your agent taps into, like pulling data from BigQuery or using Vertex AI Search.

Here’s a simplified breakdown of the main costs:

Component	Price	Billing Unit
Agent Engine (Compute)	Starting at $0.0994 (Tier 1)	per vCPU hour
Agent Engine (Memory)	Starting at $0.0105 (Tier 1)	per GiB hour
Model Usage	Varies by model	per 1,000 characters/tokens
Data & Tool Usage	Varies by service	per GB stored, query, etc.

The big catch here is pretty obvious: this pay-as-you-go model for raw infrastructure is unpredictable and can lead to some nasty surprise bills, especially when your support volume spikes. This is a stark contrast to eesel AI's pricing, which offers clear, interaction-based plans. With eesel AI, there are no per-resolution fees, so your costs are predictable and don't go up just because you're successfully helping more customers.

eesel AI's transparent pricing page, relevant to the discussion on Google Vertex AI Agent Builder updates for support use cases and their complex pricing models.

Google Vertex AI Agent Builder: A powerful toolkit for experts, but a complex project for most

So, what’s the verdict? Google Vertex AI Agent Builder is a seriously impressive platform if you have a dedicated team of AI engineers and are all-in on the Google Cloud ecosystem. It gives you incredible power to build custom, multi-agent systems from scratch.

However, for the vast majority of support and IT teams, it’s just not practical. The technical barrier is high, the setup is long and complicated, the pricing is a headache, and there’s no simple, risk-free way to roll out your automation. It’s like being handed a box of high-end car parts and being told to build the car yourself.

For teams who want to automate support workflows quickly and safely, a self-serve, fully integrated solution is almost always a better bet.

Ready to automate support in minutes, not months?

Instead of trying to piece together a complex AI toolkit from scratch, what if you could launch an AI agent that plugs directly into your helpdesk and learns from your data instantly?

eesel AI offers a refreshingly simple, self-serve platform that automates frontline support, helps agents draft replies, and triages tickets, all without needing a team of developers. You can simulate your AI on past tickets and go live with total confidence.

Start your free trial today.