Google Cloud has updated its Vertex AI Agent Builder with new observability dashboards, faster build-and-deploy tools, and stronger governance controls, aiming to make it easier for developers to move AI agents from prototype to production at scale.

The update adds an observability dashboard within the Agent Engine runtime to track token usage, latency, and error rates, along with a new evaluation layer that can simulate user interactions to test agent reliability.

Developers can now deploy agents to production with a single command using the Agent Development Kit (ADK), Google said in a blog post. New governance tools, such as agent identities tied to Cloud IAM and Model Armor, which block prompt injection attacks, are designed to improve security and compliance.

The ADK, which Google says has been downloaded more than seven million times, now supports Go in addition to Python and Java. This broader language support is aimed at making the framework accessible to a wider developer base and improving flexibility for enterprise teams building on multi-language stacks.

Google has also expanded managed services within the Agent Engine runtime. Developers can now deploy to the Agent Engine runtime directly from the ADK command-line interface without creating a full Google Cloud account. A Gmail address is enough to start using the service, with a free 90-day trial available for testing.

Agents built with Vertex AI Agent Builder can also be registered within Gemini Enterprise, giving employees access to custom-built agents in one workspace and linking internal tools with generative AI workflows.

The race to provide developer-friendly tools for creating secure and scalable agentic systems reflects a wider shift in enterprise AI. With the latest updates, Google is strengthening its position against competition that includes Microsoft’s Azure AI Foundry and AWS Bedrock.

Developer productivity gains

The updates are intended to make it easier to build and scale AI agents while enhancing governance and security controls.

“By turning orchestration, environment setup, and runtime management into managed services, Google’s Agent Development Kit cuts down on the time it takes to create and deploy software,” said Dhiraj Badgujar, senior research manager at IDC. “Vertex’s built-in model registry, IAM, and deployment fabric can shorten early development cycles for enterprises who are already using GCP.”

“LangChain and Azure AI Foundry provide for more model/cloud interoperability and manual flexibility, but they need more setup and bespoke integration to reach the same level of scalability, monitoring, and environment parity,” Badgujar added. “For new projects that fit with GCP, ADK may speed up development cycles by 2–3 times.”

Charlie Dai, VP and principal analyst at Forrester, agreed that Google’s new capabilities streamline the development process. “Compared to other offerings that often require custom pipelines and integration steps, Google’s approach can cut iteration time for teams already on Vertex AI,” Dai added.

Tulika Sheel, senior VP at Kadence International, noted that the ADK and one-click deployment in Vertex AI Agent Builder simplify agent creation by reducing setup and integration effort.

“For highly custom or niche workflows, the flexibility of open-framework solutions still wins, but for many enterprises seeking faster time-to-value, Google’s offering could be a real accelerator,” Sheel added.

The upgrade also represents a reset in how enterprises move from prototype to production, according to Sanchit Vir Gogia, chief analyst, founder, and CEO of Greyhound Research.

“For years, teams have been slowed by the hand-offs between development, security, and operations,” Gogia said. “Each phase added new tools, new reviews, and fresh delays. Google has pulled those pieces into one track. A developer can now build, test, and release an agent that already fits inside corporate policy.”

Observability and evaluation features

Analysts view Google’s new observability and evaluation tools as a significant improvement, though they say the capabilities are still developing for large-scale and non-deterministic agent workflows.

“The features in Vertex AI Agent Builder are a solid step forward but remain early-stage for complex, non-deterministic agent debugging,” Dai said. “While they provide granular metrics and traceability, integration with OpenTelemetry or Datadog is possible through custom connectors but not yet native.”

Others agreed that the tools are not yet full-stack mature. The latest updates enable real-time and retrospective debugging with agent-level tracing, tool auditing, and orchestrator visualization, along with evaluation using both metric-based and LLM-based regression testing.

“ADK gives GCP-native agents a lot of visibility, but multi-cloud observability is still not mature,” Badgujar said. “The new features make debugging non-deterministic flows a lot easier, although deep correlation across multi-agent states still needs third-party telemetry.”

Sheel echoed similar thoughts while acknowledging that the features are promising.

“At this stage, they’re still maturing,” Sheel said. “Enterprise uses with complex non-deterministic workflows (multi-agent orchestration, tool chains) will likely require additional monitoring hooks, custom dashboards, and metric extensions.”