Vertex AI Unlocks the Future of Multi-Agent Systems for the Enterprise

/ AI, Enterprise Software, Google Cloud Next 2025, Infrastructure
Vertex AI Unlocks the Future of Multi-Agent Systems for the Enterprise

The News:

At Google Cloud Next 2025, Google announced significant enhancements to Vertex AI to support the development, deployment, and management of multi-agent systems. These updates include the launch of the Agent Development Kit (ADK), Agent Engine, and the open Agent2Agent protocol. Together, they form a comprehensive and secure foundation for building and scaling intelligent agents that collaborate across platforms, data, and ecosystems. Read the full post here.

Analysis:

According to McKinsey, enterprises that move beyond experimentation to full AI production at scale can realize up to 20% cost savings and 30% faster decision-making. Vertex AI’s new capabilities unify models, data, and agents on one platform—bridging the gap between R&D and operational excellence.

By eliminating integration overhead and reinforcing enterprise-grade security, Google’s Vertex AI is uniquely positioned to define how multi-agent systems are built and deployed in the enterprise. It’s not just about building better agents—it’s about creating intelligent, collaborative systems that work in real-world environments, at scale.

Market Momentum Behind Multi-Agent Architectures

As enterprises operationalize AI, the need to scale from individual models to coordinated systems of AI agents has become clear. Industry experts project that by 2027, over 60% of AI applications will be built on multi-agent frameworks. Google’s move to unify this paradigm within Vertex AI—while maintaining model and deployment flexibility—addresses a critical pain point for organizations struggling with fragmented AI architectures.

Strategic Positioning of Vertex AI and Gemini

With Gemini 2.5’s reasoning power, Vertex AI is evolving beyond model orchestration to become an enterprise agent infrastructure platform. The Agent Development Kit (ADK) enables developers to shape agent logic and control with deterministic guardrails, while the fully managed Agent Engine simplifies production deployment. This tight integration provides the missing layer between AI experimentation and enterprise-grade execution.

Past Developer and Ops Challenges in Scaling Agents

Prior to these updates, deploying multi-agent systems involved integrating disparate frameworks, managing infrastructure manually, and debugging agents with limited traceability. Teams had to choose between agility and security, often delaying production launches due to governance hurdles. Google’s ADK and Agent Engine reduce these barriers by offering:

  • Open-source agent creation tools
  • Built-in memory and secure context management
  • Full lifecycle observability and evaluation workflows

A New Standard for AI Interoperability

Google’s Agent2Agent protocol is an open interoperability layer that allows agents built on different frameworks to communicate, negotiate tasks, and collaborate securely. With backing from over 50 partners—including Salesforce, Elastic, UiPath, and Box—Agent2Agent sets the stage for an AI ecosystem where best-in-class agents can work together regardless of their origin.

Meanwhile, ADK and MCP (Model Context Protocol) provide extensible ways to connect agents to APIs, data sources, and real-time signals without rebuilding infrastructure. Enterprises can now:

  • Ground responses using Google Maps, Google Search, or commercial data sources
  • Access 100+ connectors from Apigee and Application Integration
  • Leverage Google Cloud VPC and IAM for zero-trust access and fine-grained controls

Looking Ahead:

Google is positioning Vertex AI as the “operating system” for enterprise agentic AI. Expect additional innovations in:

  • Simulation environments for agent testing
  • Enterprise agent marketplaces via Agentspace
  • Expanded ADK language support and deployment targets
  • Embedded reasoning safety tools and agent debuggers

This consolidation eliminates the need to bolt together point solutions, enabling faster, more secure agent deployments across every vertical.

Industry-Ready Use Cases Emerging

Companies like Revionics and Renault are already deploying multi-agent systems to automate dynamic pricing and infrastructure planning, respectively. We anticipate use cases in supply chain, sales enablement, and customer service to proliferate, as agents gain long-term memory, execute code, and act on multimodal inputs.

Author

  • Paul Nashawaty, Practice Leader and Lead Principal Analyst, specializes in application modernization across build, release and operations. With a wealth of expertise in digital transformation initiatives spanning front-end and back-end systems, he also possesses comprehensive knowledge of the underlying infrastructure ecosystem crucial for supporting modernization endeavors. With over 25 years of experience, Paul has a proven track record in implementing effective go-to-market strategies, including the identification of new market channels, the growth and cultivation of partner ecosystems, and the successful execution of strategic plans resulting in positive business outcomes for his clients.

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