Adobe unveiled its enterprise-ready AI agent platform, introducing Agent Composer and an Agent SDK that allow businesses to build, configure, and deploy specialized agents across marketing and customer experience workflows. The platform connects directly to Adobe's Real-Time Customer Data Platform, Journey Optimizer, and Experience Manager, with over 70% of eligible customers already using the Adobe AI Assistant. For developers, this means access to an Agent Registry where custom agents can be built and shared, while business leaders gain six out-of-the-box agents for audience building, journey planning, and experimentation. The company is opening development through partnerships with Cognizant, Google Cloud, and PwC to extend agentic capabilities across external data ecosystems.

The Shopping Revolution Backed by Consumer Data

A comprehensive Kearney study reveals that 60% of U.S. consumers expect to use AI shopping agents within the next year, with nearly three-quarters already familiar with AI tools. This "agentic commerce" shift means AI systems will anticipate needs, compare prices, and execute purchases automatically—fundamentally changing how retailers compete. For businesses, this isn't about having a chatbot anymore; it's about ensuring your brand appears in an AI agent's decision-making process at all. The research, based on surveys of 750 consumers conducted in July and September, points to a disintermediation level not seen since ecommerce began.

Enterprise-Scale Deployment Reaches 450+ Agents

Accenture and Google Cloud announced expanded agentic AI capabilities through Gemini Enterprise, with Accenture making more than 450 agents available on Google Cloud Marketplace. Real implementations are delivering measurable results: clients including JCOM, Radisson Hotel Group, and a U.S. health insurer are solving complex business challenges with the platform. For newcomers, think of these agents as specialized digital workers that handle specific tasks—from analyzing contracts to managing customer service—but operating continuously and at scale. The joint generative AI Center of Excellence is expanding specifically to support agentic capabilities, meaning businesses can now access pre-built solutions rather than starting from scratch.

Real-World Performance Metrics Emerge

Sojern, a travel industry marketing platform, built its AI-driven audience targeting system on Vertex AI and Gemini, achieving dramatic efficiency gains: audience generation time dropped from two weeks to less than two days, while helping clients improve cost-per-acquisition by 20-50%. The system processes billions of real-time traveler intent signals to generate over 500 million daily predictions. For developers, this demonstrates how agentic AI handles complex, multi-step workflows—analyzing intent signals, building audiences, and optimizing campaigns—tasks that previously required manual coordination across teams.

Definity, working with Deloitte, deployed AI agents that summarize calls, automate authentication, and provide real-time recommendations, resulting in 20% reduction in call handle times and 15% productivity boost. Contact center agents now complete onboarding in one to two weeks instead of four to six weeks. This translates to faster time-to-value for businesses: implementation delivered measurable ROI within months, not years.

Financial Services Lead Adoption

SEB, a Nordic corporate bank supported by Bain & Company, developed an AI agent for wealth management that generates call summaries and suggested responses, increasing efficiency by 15%. United Wholesale Mortgage more than doubled underwriter productivity in just nine months using Vertex AI and Gemini, directly shortening loan close times for 50,000 brokers. For business leaders evaluating agent adoption, these financial services implementations demonstrate that regulated industries with complex compliance requirements are successfully deploying agents—addressing common concerns about risk and oversight.

Banco Covalto in Mexico reduced credit approval response times by more than 90% using generative AI to streamline processes. Safe Rate created an AI mortgage agent with features like "Beat this Rate" that help borrowers get personalized quotes in under 30 seconds. For newcomers, these examples show agents handling high-stakes decisions that previously required extensive human review, but with built-in guardrails that maintain accuracy and compliance.

Security Considerations Take Center Stage

Zenity hosted an AI Agent Security Summit where experts defined agents as "systems that pursue complex goals with limited supervision" and highlighted emerging security risks. The discussion centered on agents as potential "malicious insiders" that operate faster than humans, with specific focus on attacks targeting AI agents and coding tools like the recent compromise of the Amazon Q extension for Visual Studio Code. For developers, this emphasizes the importance of security-first design: as Simon Willison formulated it, agents are "AI models using tools in a loop"—each tool access point represents a potential vulnerability. Companies building agents must now consider security beyond content safety, particularly when agents can control systems and access sensitive data.

What This Means

For developers: Multiple enterprise platforms now offer SDKs, registries, and marketplaces for agent development, lowering the barrier to building production-ready solutions. The focus has shifted from proof-of-concept to scalable, secure implementations with clear integration paths.

For business leaders: ROI data from early adopters shows 15-90% efficiency improvements across industries, with implementation timelines measured in months. The question is no longer whether to adopt agents, but which workflows to automate first and how to ensure competitive relevance as consumer expectations shift rapidly.

For newcomers: AI agents have moved from experimental technology to practical tools handling real business processes—from approving loans to managing customer service. The technology works by combining multiple AI capabilities (understanding requests, accessing data, taking actions) in automated loops, with human oversight built into critical decision points.