Custom Agents, Little Coding All about Google’s Vertex AI Agent Builder

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Custom Agents, Little Coding: All about Google’s Vertex AI Agent Builder

Google is empowering developers to build autonomous agents using little or no custom code.

What’s new: Google introduced Vertex AI Agent Builder, a low/no-code toolkit that enables Google’s AI models to run external code and ground their responses in Google search results or custom data.
How it works: Developers on Google’s Vertex AI platform can build agents and integrate them into multiple applications. The service costs $12 per 1,000 queries and can use Google Search for $2 per 1,000 queries.

  • You can set an agent’s goal in natural language (such as “You are a helpful assistant. Return your responses in markdown format.”) and provide instructions (such as “Greet the user, then ask how you can help them today”). 
  • Agents can ground their outputs in external resources including information retrieved from Google’s Enterprise Search or BigQuery data warehouse. Agents can generate a confidence score for each grounded response. These scores can drive behaviors such as enabling an agent to decide whether its confidence is high enough to deliver a given response.
  • Agents can use tools, including a code interpreter that enables agents to run Python scripts. For instance, if a user asks about popular tourist locations, an agent can call a tool that retrieves a list of trending attractions near the user’s location. Developers can define their own tools by providing instructions to call a function, built-in extension, or external API.
  • The system integrates custom code via the open source library LangChain including the LangGraph extension for building multi-agent workflows. For example, if a user is chatting with a conversational agent and asks to book a flight, the agent can route the request to a subagent designed to book flights.

Behind the news: Vertex AI Agent Builder consolidates agentic features that some of Google’s competitors have rolled out in recent months. For instance, OpenAI’s Assistants API lets developers build agents that respond to custom instructions, retrieve documents (limited by file size), call functions, and access a code interpreter. Anthropic recently launched Claude Tools, which lets developers instruct Claude language models to call customized tools. Microsoft’s Windows Copilot and Copilot Builder can call functions and retrieve information using Bing search and user documents stored via Microsoft Graph.

Why it matters: Making agents practical for commercial use can require grounding, tool use, multi-agent collaboration, and other capabilities. Google’s new tools are a step in this direction, taking advantage of investments in its hardware infrastructure as well as services such as search. As tech analyst Ben Thompson writes, Google’s combination of scale, interlocking businesses, and investment in AI infrastructure makes for a compelling synergy. 

We’re thinking: Big-tech offerings like Vertex Agent Builder compete with an expanding universe of open source tools such as AutoGen, CrewAI, and LangGraph. The race is on to provide great agentic development frameworks!


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