Actually, I used this Vertex AI agent builder to make a chatbot for my product that helps users to upload their documents, and the chatbot developed from Vertex AI agent builder will answer questions as per the attached document and provide any other required information. It has great intuitive interface and after building the bot I easily integrated t in my product. there is a perfect documentation provided by them so no confusion niether during building nor at the time of integration. work very fine and fast as compared to other AI agents. Review collected by and hosted on G2.com.

After the successful chatbot development, I used it to make an AI video generator, but as I tried to increase the length of the AI video, the cost increased by almost 50%. so I left the idea of AI video generator. Review collected by and hosted on G2.com.

I like how quickly we were able to build AI Agents without managing the entire LLM infrastructure overselves. The integration with vertex AI models, Google Search and enterprise data sources made it much easier to create internal support assistants.

It's built-in grounding and function calling capabilities really improved the quality of responses compared to basic chatbots. One unexpected benefits was how easily we could connect the agent with existing APIs which reduced manual work for our support team and improved response time for employees. Review collected by and hosted on G2.com.

The documentation is good but some advanced agent configurations still require a lot of trial and error. Pricing can also become expensive if the agents receive high traffic or use larger models frequently. I also wish there were more ready made templates for enterprise use cases. Better debugging tools for conversation flows would make development much easier. Review collected by and hosted on G2.com.

What I like best about Vertex AI Agent Builder is that it makes it easier to create AI agents without building everything from scratch. It provides useful tools for connecting data, building conversational experiences, testing agent behavior, and improving responses.

It is especially helpful because it combines Google Cloud’s AI capabilities with a structured way to build, deploy, and manage AI agents more efficiently. Review collected by and hosted on G2.com.

What I dislike about Vertex AI Agent Builder is that it can feel complex for beginners, especially when setting up integrations, data sources, and advanced agent workflows. Some features may require good knowledge of Google Cloud, so there is a learning curve at the start. Review collected by and hosted on G2.com.

What I like best about Vertex AI Agent Builder is how quickly it enables us to build, deploy, and scale AI agents with minimal development effort. Its seamless integration with Google Cloud services, strong support for generative AI models, and flexible customisation options make it easy to create intelligent, production-ready applications Review collected by and hosted on G2.com.

One drawback is that the platform has a learning curve for advanced workflows, especially for teams new to Google Cloud. Debugging complex agent behavior can also be challenging, and I'd like to see more prebuilt templates, richer documentation, and broader third-party integrations to speed up development. Review collected by and hosted on G2.com.

What I like most about Vertex AI Agent Builder is the speed with which you can create useful assistants without having to develop the entire infrastructure from scratch. In our PPC agency, we have used it to prototype agents that help analyze search terms, summarize performance changes, and answer questions about campaigns by connecting to Google Ads data and internal documents. The integration with the Google Cloud ecosystem and the Gemini models makes it much easier to take an idea to a real test with clients in just a few days. I would also highlight the ability to combine structured information (reports, feeds, spreadsheets) with natural language, which makes the agent more practical for marketing teams that are not technical. Review collected by and hosted on G2.com.

Although it is a very powerful platform, the initial learning curve can be somewhat steep, especially for marketing teams without a technical profile. Setting up truly useful agents requires dedicating time to properly define data sources, instructions, and tests. Additionally, some more advanced integrations and customization features depend on the Google Cloud ecosystem, so you get much more value if you already work with that infrastructure. We would also like to have more specific templates for marketing and PPC use cases. Review collected by and hosted on G2.com.

I primarily use Vertex AI Agent Builder with the Agent Development Kit (ADK) and Agent Engine. I like how it streamlines the process of building, testing, and deploying AI agents on Google Cloud. The integration with the GCP ecosystem and managed deployment through Agent Engine reduces operational overhead and lets me focus more on agent development than infrastructure. Review collected by and hosted on G2.com.

While building and deploying agents is relatively straightforward with the vertex AI Agent builder, debugging deployment issues and understanding runtime errors can sometimes be difficult. Better diagnostics and more detailed documentation would improve the developer experience. Review collected by and hosted on G2.com.

What I like most about Vertex AI Agent Builder is how quickly it lets me develop and deploy AI-powered agents with minimal infrastructure setup. The seamless integration with Google Cloud services, along with support for generative AI models, built-in retrieval capabilities, and flexible orchestration, makes it straightforward to create scalable, production-ready conversational experiences. I also appreciate the intuitive interface and the available customization options, which help speed up development while still giving me control over the agent’s behavior. Review collected by and hosted on G2.com.

Vertex AI Agent Builder is powerful, but the learning curve can feel steep for new users, particularly when setting up advanced workflows and integrations. Debugging agent behavior and tracing execution paths aren’t as intuitive as they could be, and pricing can become hard to predict as usage scales. More built-in templates, clearer documentation for complex scenarios, and stronger monitoring tools would go a long way toward improving the overall developer experience. Review collected by and hosted on G2.com.

Vertex AI gives me a broad range of models to choose from, which makes experimentation much easier. I like that the platform keeps everything in one place, training, tuning, and deployment, so I don’t have to bounce between different tools. Overall, it’s straightforward to use, scales well, and integrates smoothly with the rest of GCP. Review collected by and hosted on G2.com.

The learning curve can be a bit steep, especially for new users who aren’t already familiar with GCP. Some workflows feel more complex than they need to be. Pricing is also on the higher side Review collected by and hosted on G2.com.

Simply connecting my own data, the most wonderful thing! We are using this daily so as to feed the AI algorithms good information. The RAG system is very good. I simply hook up my Google Cloud Storage or BigQuery account, and the agent runs the hard work for me under the hood.

Agent Studio is amazing as I can try things without writing a lot of code for databases. So in an hour I can create a basic chat bot to present to my manager. The Python SDK is quite good, if I wish to do more complicated work. Understands me very well when I am speaking and uses Gemini all the time without error. Review collected by and hosted on G2.com.

Very confusing pricing, sometimes my bill goes crazy. Besides, the documentation is poor and it's very difficult to set up GCP permission for a common user such as myself. Review collected by and hosted on G2.com.

I open Gemini pretty much every day to help me brainstorm ideas, draft emails, and sort through some technical stuff. I find it incredibly helpful for hands-on tasks like researching ideas, structuring feasibility studies, and crunching the numbers for various projects. It seamlessly pulls together deep research, structures step-by-step feasibility studies, and handles operational calculations all in one conversation, making day-to-day tasks much easier. Also, its capacity to assist with information retrieval and research adds significant value to my work. Review collected by and hosted on G2.com.

I find error cascades in multi-step workflows and integration fragility challenging. There's also a steep learning curve and setup complexity involved. Review collected by and hosted on G2.com.