VMware Tanzu announced today the availability of new GenAI capabilities in VMware Tanzu Platform, a private, pre-engineered, AI-ready platform-as-a-service (PaaS). This includes support for the new Model Context Protocol (MCP) defined by Anthropic which allows customers to connect to existing enterprise data and systems, as well as easily connect to a growing ecosystem of MCP-enabled tools and models. These new capabilities enable Tanzu customers to rapidly build AI and agentic applications that perform complex tasks with their enterprise context and guardrails. In addition, Broadcom announces the availability of the MCP Java SDK within the Spring community to accelerate the adoption of agentic development in the enterprise.
Tanzu Platform helps enterprises keep pace with agentic evolution
Following the announcement of VMware Tanzu AI Solutions last summer at VMware Explore 2024, the upcoming Spring release of Tanzu Platform will expand support for GenAI and agentic use cases. Agentic AI workflows are no longer a futuristic concept. Agentic applications are attainable and necessary for businesses that want to drive exponential growth. According to Gartner, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024 (Source: Gartner TSP 2025 Trends: Agentic AI — The Evolution of Experience, 24 February 2025, ID G00823982).
Broadcom has emerged as an industry-leading provider of custom AI silicon and an enabler of private AI cloud computing infrastructure. Now, with its AI-ready PaaS, optimized for private cloud, the Tanzu Division of Broadcom will give enterprises the means to transform GenAI and agentic use cases into code, combine it with valuable and closely held data, and can deliver code to production in minutes, reducing time to market and improving ROI.
“Conceptually, business leaders understand that agentic AI will be a catalyst for substantial cost savings and efficiencies. Repetitive, high volume tasks previously completed by humans can now be done through agentic applications and can be scaled up or down based on changing business conditions. However, the practical implementation of agentic AI – an enterprise virtual assistant for example – can be complex, especially if data resides in multiple sources,” said Purnima Padmanabhan, general manager, Tanzu Division, Broadcom. “We are thrilled to provide a simple way for customers to get started with Tanzu Platform for agentic development. It delivers the governance enforcement tools, data access and model brokering that customers need to build GenAI and agentic AI applications with a very low barrier to entry.”
Over a decade ago, Tanzu pioneered an application PaaS designed to abstract away infrastructure concerns for enterprise developers building cloud native microservices. Tanzu Platform’s highly opinionated infrastructure combined with the ability to seamlessly connect data to enterprise applications is a proven approach that can also be leveraged for GenAI and agentic application development. GenAI and agentic workflows are essentially microservices that require more iteration, more integration, and new model services to call.
Tanzu can accelerate GenAI application delivery at two levels: First, Spring AI brings the power of GenAI patterns to Java developers who are writing and maintaining the majority of business applications in the enterprise. Second, Tanzu Platform takes source code written in any language, including Python applications popular with data science teams, and provides a more secure, one-click “push” to production, with integrated data services and no-code AI/ML model swapping that enables faster, more frequent iterations.
Spring AI is turning millions of Java developers into agentic developers
Spring AI continues to simplify GenAI application development and allow the enterprise developers familiar with the business logic and systems already in use to quickly adopt and expand agentic use cases. Capabilities include the templates for common GenAI application patterns like chatbots and RAG combined with common data processing tasks, chat memory support, tools/function calling, abstracted API support for vector stores and support for all major vector database providers. Spring AI also provides baked-in observability and utilities for AI model evaluation so that applications and models may be tuned for quality and performance over time.
Spring AI added an implementation of the Model Context Protocol (MCP) within weeks of the specifications release, to provide a modular architecture that enables seamless integration of AI applications with local and remote data sources and tools via more secure, standardized connections, supporting a wide range of use cases from simple file access to complex, multi-model connectivity for agent-based applications. The Spring AI MCP implementation designed by Tanzu, has been modified for use outside of the Spring Framework and has become the official Model Context Protocol (MCP) Java SDK.
Tanzu Platform delivers simplicity and safety for developers, data science teams, and platform engineers
Tanzu Platform was built for agentic and GenAI applications. For application developers, regardless of developer framework or language, Tanzu delivers the simple “cf-push” experience that includes automated, more secure container builds, instantiates dependencies like local models or data stores, connects to a rich ecosystem of external models, services, pipelines, and processes, and deploys or updates applications without ever raising a ticket or inputting a static credential. For data science or AI teams, they get a central place to review application interactions to evaluate model performance and quality, and swap out or update models as needed without ever touching the application code. Finally, platform teams can curate the authorized models available to developers, automating token rate limiting to manage costs, and continuously repaving and updating the applications and underlying operating system and runtime layers to limit exposure to zero day attacks and CVEs.
In the Tanzu Platform Spring 2025 release, Tanzu Platform expands its model provider ecosystem to include an integration of Anthropic and its large language model, Claude. The update improves model journaling capabilities such as the ability to review interactions and export the journal results in formats consistent with major model provider's distillation functionality. This integration with the Anthropic API enables organizations to seamlessly proxy requests to Claude models while maintaining robust governance controls. This includes Role-Based-Access-Control, rate limiting to AI applications using the Claude model, the ability to review the interactions in agentic flows, and the ability to use the most efficient model possible.
Collaboration with Anthropic on the MCP Java SDK
MCP was announced by Anthropic in November 2024 and within weeks, Tanzu introduced Spring AI MCP, enabling millions of enterprise Java developers to utilize MCP. In February 2025, Tanzu announced the donation of a Java SDK to the open source community, which was then adopted by Anthropic as the official Java SDK for MCP. This robust Java SDK implementation of the MCP brings standardized AI model integrations capabilities to the Java community.
With Spring as a leader in enterprise development and the rapid growth of agentic app patterns, Anthropic and the Spring AI team will continue to evolve the Java SDK for MCP, enabling the latest in data accessibility and tools interoperability for Gen AI application development.
Learn more about agentic development with Tanzu
Learn more about new Tanzu innovation enabling faster GenAI and agentic development by watching our virtual event, Racing Toward AI App Delivery with Tanzu: Navigating the Fast Lane with Intelligence. Register or view the replay for an in-depth discussion on how business leaders are leveraging AI in their application strategy featuring Purnima Padmanabhan, Tanzu GM and VP, Adib Saikali, Tanzu Distinguished Engineer and Michael Coté, Senior Member of Technical Staff; plus lightboard session and demo that illustrate how Tanzu empowers teams to develop and iterate AI apps faster.