New Reasoning Models and Agent Capabilities Promise To Transform Business Applications
In today’s rapidly evolving AI landscape, enterprises need reliable platforms that combine powerful models with practical deployment capabilities. Google Cloud’s latest enhancements to Vertex AI and the Gemini model family offer businesses a comprehensive solution for building, deploying, and managing AI applications with unprecedented speed and efficiency. Vertex AI is Google Cloud’s platform to orchestrate the three pillars of production AI: models, data, and AI agents.
Google Cloud has significantly enhanced its Vertex AI platform with new capabilities centered around reasoning models and agent ecosystems, improving the ability for enterprises to build and deploy artificial intelligence applications. The Vertex AI platform now supports over 200 models besides Google’s. The cloud provider’s latest Gemini 2.5 models represent a fundamental shift from simple response generation to what Google calls “reasoning models” – AI systems that demonstrate transparent step-by-step thinking before producing outputs. Reasoning models can work through complex analyses across multiple information sources and make nuanced decisions based on enterprise data and
Google offers two complementary models targeting different business needs. Gemini 2.5 Pro, designed for complex problem-solving with a one-million token context window, enables sophisticated analysis of extensive documents and codebases. Meanwhile, Gemini 2.5 Flash offers optimized performance for high-volume, cost-sensitive applications where efficiency at scale is paramount.
Organizations have faced insurmountable barriers to developing trust in AI outputs without understanding how AI arrives at conclusions. The first step in this process was listing the sources AI used in responses. Still, reasoning models enhance this by demonstrating their thought process, marking a critical advancement for enterprises requiring explainable AI for compliance and governance requirements.
The availability of a combination of solutions that offer cost, performance, and transparency is a step in the right direction for supporting the wide range of enterprise AI requirements. Early adopters report compelling results. Moody’s claims Google’s Solution provided over 95% accuracy and an 80% reduction in processing time for complex financial document analysis. Box has implemented AI extract agents for unstructured data processing across procurement and reporting workflows, demonstrating practical applications in information management. But it takes more than AI models to build robust strategies.
Bolstering AI Agent Capabilities With New Tools
The number one agentic AI concern enterprise buyers have expressed to Lopez Research is fear that agents will make and implement the wrong decision. Many organizations shared concern that AI orchestration solutions are half-baked, and there’s fear that agents won’t operate properly because the data and work streams required to complete a task span multiple applications and services. To solve this, companies are looking for robust AI orchestration to coordinate and manage various AI systems, models, or components to work together seamlessly in solving complex tasks. Finally, it’s not as easy as you click a button and deploy an army of agents. Companies need tools that help them more easily build and deploy custom and out-of-the-box agents faster.
New Solutions Aim to Overcome Enterprise AI Deployment Concerns
To address these concerns, Google announced a wave of new multiagent ecosystem capabilities in its Vertex AI that allow multiple AI systems to work together to accomplish complex tasks. The company introduced several components to enable this approach, including the Agent Development Kit (ADK), the Agent2Agent protocol, Agent Engine, and updates to Agentspace.
Minimizing the Data Collaboration Problem with the Agent2Agent Protocol
Most vendors claim they can provide fully autonomous AI agents. Still, most buyers prefer to deploy these agents semi-autonomously to reduce concerns about process failures or inaccuracies. To address the enterprise buyer issue of data access and execution across various applications, Google introduced the Agent2Agent protocol, an open standard for enabling communication between agents built on different frameworks and vendors. Google launched the protocol with the support of over 50 industry partners, including Salesforce, ServiceNow, and UiPath. The Agent2Agent initiative addresses one of the most significant barriers to enterprise AI adoption: painful integration challenges to create interoperability across disparate systems.
Making it Easier for Developers of All Skill Levels to Build AI
Meanwhile, the Agent Development Kit (ADK), agent engine, and other advances in the Vertex AI platform help bootstrap the development of agents. Agent Development Kit, an open-source framework, allows developers to build sophisticated agents with approximately 100 lines of code –dramatically reducing development complexity. It also offers pre-built samples through Agent Garden to further accelerate development. ADK offers compatibility with over 200 models from providers like Anthropic, Meta, and Mistral AI.
The companion Agent Engine provides a fully managed runtime for deployment, eliminating the traditional challenges such as rebuilding the agent to move from prototype to production. Agent engine also provides evaluation tools to measure and improve agent quality.
Security and data integration capabilities round out the platform, with configurable content filters, identity controls, and Google Cloud’s Virtual Private Cloud (VPC) service controls providing multi-layered protection. Equally valuable is the platform’s ability to connect agents to enterprise data through various methods, including standard protocols and direct API integration.
Improving Access to AI Agents
Once a company can design, manage, and secure agents, the biggest obstacle to success is getting agents ubiquitously adopted within the enterprise. Agentspace aims to help employees find, publish, and consume agents. Agentspace, launched in December 2024, allows employees (and agents) to find information from across their organization, synthesize and understand it with Gemini’s multimodal intelligence, and act on it with AI agents. Enterprises can discover and adopt agents quickly and easily with Agent Gallery and create agents with Google’s no-code Agent Designer. Firms can also deploy Google-built agents, such as its new Deep Research and Idea Generation agents, to help employees generate and validate business ideas and synthesize dense information.
At the conference, Google announced that Agentspace is integrated with Chrome Enterprise, letting employees leverage Agentspace’s unified search capabilities from the Chrome search box. Bringing Agentspace directly into Chrome will help employees easily and securely find information, including data and resources, right within their existing workflows.
Perhaps what was most surprising was to learn that actual businesses are deploying agents today. Client quotes during the keynote and on Google Cloud’s website demonstrated that business impact is already evident across diverse industries. For example, Revionics has implemented a multiagent system for optimizing retail pricing, while Renault Group developed agents to strategically place EV charging infrastructure using geographical analysis. Gordon Food Service is using Agentspace to change how it accesses enterprise knowledge with searches grounded in its data across Google Workspace and other sources like ServiceNow. These early examples demonstrate the potential for complex automation of previously human-intensive analytical workflows.
The key takeaway: AI Agents Will Happen
The strategy provides elements for sophisticated developers, novice designers, and employees who must find and use agents to improve their workflow. The availability of models, connectors, and out-of-the-box agents will help eliminate painful trade-offs between model capability, enterprise integration, and production readiness. The result isn’t merely faster development but significantly more reliable agents prepared for mission-critical enterprise workflows.
As reasoning models and multi-agent systems evolve from experimental concepts to production realities, organizations should evaluate not only the capabilities of individual models but also the broader infrastructure required for responsible enterprise deployment. The key consideration for executives evaluating AI investments isn’t individual technical capabilities but rather the breadth of the portfolio and ecosystem to accelerate time-to-value while maintaining governance requirements. Google’s latest enhancements to Vertex AI and AI agent tooling suggest a maturing approach focused on practical enterprise adoption rather than merely advancing technical benchmarks.