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The agent builder arms race continues as Google Cloud pushes deeper into orchestration and ops

The march towards agentic enterprises continues as companies battle to keep developers on their platforms throughout the entire agent lifecycle. Google Cloud has updated its Agent Builder on Vertex AI, introducing additional governance tools for enterprises and expanding the capabilities for creating agents with just a few lines of code. Agent Builder, released last year during its annual Cloud Next event, provides a no-code platform for enterprises to create agents and connect these to orchestration frameworks like LangChain. Google’s Agent Development Kit (ADK), which lets developers build agents “in under 100 lines of code,” can also be accessed through Agent Builder. The new updates include features to build agents faster with state-of-the-art context management layers and one-click deployment, as well as managed services for scaling production and evaluation, and support for identifying agents.“These new capabilities underscore our commitment to Agent Builder, and simplify the agent development process to meet developers where they are, no matter which tech stack they choose,” said Mike Clark, director of Product Management, Vertex AI Agent Builder. 
Building agents fasterPart of Google’s pitch for Agent Builder’s new features is that enterprises can bake in orchestration even as they construct their agents. “Building an agent from a concept to a working product involves complex orchestration,” said Clark. The new capabilities, which are shipped with the ADK, include:SOTA context management layers including Static, Turn, User and Cache layers so enterprises have more control over the agents’ contextPrebuilt plugins with customizable logic. One of the new plugins allows agents to recognize failed tool calls and “self-heal” by retrying the task with a different approachAdditional language support in ADK, including Go, alongside Python and Java, that launched with ADKOne-click deployment through the ADK command line interface to move agents from a local environment to live testing with a single command
Governance layerEnterprises want agents doing work for them to be accurate, perform well and something they can control if something does go wrong.While Google had observability features in the local development environment at launch, developers can now access these tools through the Agent Engine managed runtime dashboard. The company said this brings cloud-based production monitoring to track token consumption, error rates and latency. Within this observability dashboard, enterprises can visualize the actions agents take and reproduce any issues. Agent Engine will also have a new Evaluation Layer to help “simulate agent performance across a vast array of user interactions and situations.”This governance layer will also include:Agent Identities that Google said give “agents their own unique, native identities within Google Cloud Model Armor, which would block prompt injections, screen tool calls and agent responsesSecurity Command Center, so admins can build an inventory of their agents to detect threats like unauthorized access“These native identities provide a deep, built-in layer of control and a clear audit trail for all agent actions. These certificate-backed identities further strengthen your security as they cannot be impersonated and are tied directly to the agent's lifecycle, eliminating the risk of dormant accounts,” Clark said. The battle of agent builders It’s no surprise that model providers create platforms to build agents and bring them to production. The competition lies in how fast new tools and features are added.Google’s Agent Builder competes with OpenAI’s open-source Agent Development Kit, which enables developers to create AI agents using non-OpenAI models. Additionally, there is the recently announced AgentKit, which features an Agent Builder that enables companies to integrate agents into their applications easily. AWS also offers agent builders on its Bedrock platform.However, it isn’t just companies with their own models that court developers to build their AI agents within their platforms. Any enterprise service provider with an agent library also wants clients to make agents on their systems. Capturing developer interest and keeping them within the ecosystem is the big battle between tech companies now, with features to make building and governing agents easier.