Google Cloud made a major update to keep AI ddevelopers on the Vertex AI platform to conceptualize, design, build, test, deploy and modify AI agents for enterprise applications.
Modern features announced today include additional enterprise management tools and expanded agent creation capabilities with just a few lines of code, faster performance with state-of-the-art context management layers and one-click deployment, and managed services to scale production and evaluation and support agent identification.
Agent Builder, released last year at the annual event, Cloud Next provides enterprises with a no-code platform to create agents and connect them to orchestration platforms like LangChain.
Google Agent Development Kit (ADK), which allows developers to create agents “in less than 100 lines of code,” is also available through Agent Builder.
“These new capabilities underscore our commitment to Agent Builder and simplify the agent development process to meet developers where they are, no matter what technology stack they choose,” said Mike Clark, director of product management at Vertex AI Agent Builder.
Create agents faster
Part of Google’s proposal for novel Agent Builder features is that enterprises can create orchestrations while building their agents.
“Building an agent from concept to working product requires complex orchestration,” Clark said.
Modern capabilities shipped with the ADK include:
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SOTA context management layers, including stationary, turn-based, user and cache layers, giving enterprises greater control over agent context
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Ready-made plugins with configurable logic. One of the novel plugins allows agents to recognize failed tool calls and “self-repair” by retrying the task with a different approach
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Additional language support in the ADK, including Go, in addition to Python and Java that launched with the ADK
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One-click deployment via the ADK command-line interface to move agents from on-premises to live testing with a single command
Management layer
Enterprises require high accuracy; security; observability and auditability (what the program did and why); and steerability (control) in their production-grade AI agents.
While Google provided observability features in the local development environment at launch, developers can now access these tools through a dashboard managed by Agent Engine.
The company said this provides cloud production monitoring to track token consumption, error rates and latency. From this dashboard, enterprises can visualize actions taken by agents and reproduce any issues.
Agent Engine will also feature a novel evaluation layer that will lend a hand “simulate agent performance across a wide range of user interactions and situations.”
This level of management will also include:
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Agent identities, which Google says give “agents their own unique, native identities in Google Cloud
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An Armor model that would block rapid injections, screen tool calls, and agent responses
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Security Command Center, so administrators can inventory their agents to detect threats such as unauthorized access
“These native identities provide a deep, built-in layer of control and a clear audit trail for all agent activities. These certificate-backed identities further strengthen security because they cannot be spoofed and are tied directly to the agent lifecycle, eliminating the risk of inactive accounts,” Clark said.
Agent Builder Battle
It’s no surprise that model vendors are creating platforms to build agents and put them into production. The competition comes down to how quickly novel tools and features are added.
Google’s competition is Agent Builder OpenAIis open source software Agent Development Kitwhich allows developers to create AI agents using models other than OpenAI.
Plus it’s recent announced AgentKitwhich includes an Agent Builder tool that allows companies to easily integrate agents into their applications.
Microsoft has its own Azure AI Foundrylaunched last year around this time to create AI agents, and AWS also offers agent builders on his Bedrock platform, but Google hopes a set of novel features will give it a competitive advantage.
However, it’s not just companies with their own models that are encouraging developers to create AI agents on their platforms. Every enterprise service provider with a library of agents also wants customers to create agents in their systems.
Capturing developer interest and keeping them in the ecosystem is a gigantic battle among tech companies today, and features make it easier to create and manage agents.