The web is undergoing its most profound transformation since the mobile revolution. For decades, we’ve navigated digital spaces through a familiar paradigm: click buttons, fill forms, navigate menus. This static, input-response model has defined web interactions since the earliest days of the internet. But in 2025, a fundamental shift is underway—one that promises to be as revolutionary as the transition from desktop to mobile.
Welcome to the era of agentic web experiences, where AI agents don’t just respond to clicks—they anticipate needs, execute complex workflows, and operate autonomously on behalf of users.
What Are Agentic Web Experiences?
Agentic web experiences represent a paradigm where agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users, transitioning from human-driven to machine-to-machine interaction. Unlike traditional web interfaces built around static buttons and predetermined user flows, agentic systems introduce autonomous decision-making into the digital experience.
As Nvidia CEO Jensen Huang stated on the No Priors Podcast: “there’s no question we’re gonna have AI employees of all kinds” that would “augment every single job in the company”.
The distinction is crucial. Where traditional UX design assumes users initiate every action and systems simply respond, agentic AI replaces this input-response loop with delegation, where users express intentions rather than give instructions.
The Limitations of Traditional Static Buttons
For decades, button design has been the cornerstone of web interaction. Designers have meticulously crafted rectangular shapes with rounded corners, perfected hover states, and optimized placement to guide user behavior. Yet this approach has inherent limitations:
- Linear Interaction: Every action requires explicit user input
- Limited Context: Buttons lack awareness of user intent beyond the immediate click
- Rigid Workflows: Users must follow predetermined paths designed by developers
- Cognitive Load: Users must understand what each button does and when to use it
Traditional UX design assumed a simple model: the user initiates, the system responds. This model worked beautifully for straightforward tasks, but it breaks down when users need to accomplish complex, multi-step objectives that span multiple systems and require contextual decision-making.
The Agentic Revolution: By the Numbers
The shift to agentic experiences isn’t theoretical—it’s happening at unprecedented speed:
Market Growth and Adoption
- The agentic AI market reached $5.25 billion in 2024, growing at a compound annual rate of 43.84%
- The broader AI agents market reached $7.92 billion in 2025 with projections extending to $236.03 billion by 2034
- Gartner predicts that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025
Enterprise Adoption
- 79% of organizations say AI agents are already being adopted in their companies
- 88% of executives say their companies plan to increase AI-related budgets this year due to agentic AI
- McKinsey reports that regular generative-AI use rose from 65% in 2024 to 71% in 2025 across business functions
Projected Impact
- By 2028, 33% of enterprise software applications will embed agentic AI capabilities, compared to almost none in 2023
- Gartner predicts that 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from none in 2024
- Agentic AI could drive approximately 30% of enterprise application software revenue by 2035, surpassing $450 billion, up from 2% in 2025
Real-World Success Stories
The impact of agentic experiences is already measurable:
Salesforce: Salesforce has closed 18,000 Agentforce deals since the product launched in October 2024, demonstrating strong enterprise appetite for agentic capabilities.
Capital One: The credit card giant launched Chat Concierge for auto dealership customers. Chat Concierge has been embraced by dealers because it has dramatically increased customer engagement and is 55% more successful in converting prospects.
Microsoft: At Microsoft Build, the company introduced its vision of the open agentic web, where AI agents make decisions and perform tasks on behalf of users or organizations.
The New UX Paradigm: Designing for Agency
Designing for agentic experiences requires fundamentally different principles than traditional web design:
1. Intent-First Architecture
Agentic experience design shifts from user-journey mapping to intent-system mapping, focusing on what the user wants to accomplish and which agents have relevant capabilities. Instead of mapping button clicks, designers now map intentions and outcomes.
2. From Control to Collaboration
Agents should broaden and scale human capacities, fill in knowledge gaps, facilitate collaboration, and make us better versions of ourselves. The goal isn’t to replace human decision-making but to augment it.
Microsoft’s design principles emphasize that agents should be easily accessible yet occasionally invisible: an agent largely operates in the background and only nudges when relevant and appropriate.
3. Trust Through Transparency
When an AI system takes action in the world, UX design defines not just how it feels but how safe it is, how fairly it behaves, and how much power it should hold. Users need visibility into agent decision-making processes.
Key transparency requirements include:
- Clear communication of agent intent and rationale
- Visible system state and progress indicators
- Easy escalation paths when human intervention is needed
- Audit trails for autonomous actions
4. Multi-Agent Orchestration
Interfaces must handle coordination across native desktop apps, mobile apps, web services, custom agents, and cloud-based AI services. Tomorrow’s experiences won’t be single-agent but orchestrated networks of specialized agents working in concert.
Google’s recently introduced A2UI (Agent-to-Agent User Interface) project addresses this challenge. A2UI allows agents to generate contextually relevant interfaces so they can respond to users, enabling agents to generate the interface which best suits the current conversation.
From Buttons to Conversations: The Interface Evolution
The shift to agentic experiences doesn’t mean buttons disappear—it means they become one tool among many in a more sophisticated interaction vocabulary:
- Natural Language: Users describe goals rather than navigate menu structures
- Proactive Assistance: Systems anticipate needs based on context and history
- Dynamic Interfaces: UI elements generate on-demand based on current tasks
- Cross-Platform Continuity: Agents maintain context across devices and sessions
As one UX designer reflected: We’re shifting from designing static interfaces to designing agentic experiences, and traditional UX design principles are inadequate for this new reality.
Implementation Challenges and Best Practices
Despite the momentum, organizations face significant challenges in implementing agentic experiences:
Current Obstacles
- Many agentic AI implementations are failing, though leading organizations that are reimaging operations and managing agents as workers are finding success
- Agents tend to be very ineffective because humans are very bad communicators; we still can’t get chat agents to interpret what you want correctly all the time
- Data silos, informal context, and insufficient integration plague early deployments
Success Factors
Organizations seeing positive results share common characteristics:
- Start with clear ROI: Front-end processes require a material ROI signed off by the finance partner and business unit head, keeping experiments as experiments
- Focus on processes, not people: Apply AI to specific workflows with measurable outcomes
- Invest in orchestration: 94% of organizations view process orchestration as essential for deploying AI effectively
- Prioritize governance: Establish policies for agent behavior, data access, and human oversight from day one
- Iterate methodically: Among companies using generative AI, 25% are launching pilots in 2025, doubling to 50% by 2027, showing methodical enterprise adoption
Industry-Specific Applications
Agentic experiences are transforming sectors across the economy:
Customer Service: Agents handle complex multi-turn conversations, access customer history across systems, and escalate to humans only when necessary.
Healthcare: Stanford Health Care is using Microsoft’s healthcare agent orchestrator to build and test AI agents that can help alleviate administrative burden and speed up workflow for tumor board preparation.
Financial Services: Banks deploy agent-driven fraud detection systems that process signals, correlate events, and escalate issues in real-time.
Software Development: 50% of developers now use AI coding tools daily, with 65% usage in top-quartile organizations, with GitHub Copilot evolving from in-editor assistant to asynchronous coding agent.
The Road Ahead: Four Levels of Agentic Maturity
Tech giants have crystallized the progression of agentic capabilities into four levels:
Level 1: LLM-Powered Information Retrieval – Knowledge assistants and copilots that enhance search and content generation
Level 2: Single-Task Agentic Workflows – Task-doers with self-contained action loops that complete specific jobs autonomously
Level 3: Cross-System Orchestration – Complex workflow execution across multiple platforms with supervised automation
Level 4: Multi-Agent Constellations – Loosely coupled collaborative agents that discover, communicate, and coordinate with minimal human oversight
Most organizations today operate at Levels 1-2. The companies pulling ahead are already building Level 3 capabilities, with Level 4 representing the ultimate vision of the agentic web.
What This Means for Businesses
The message for business leaders is clear: C-level executives at software organizations have a crucial three- to six-month window to define their agentic AI product strategy, as the industry is at an inflection point.
Organizations should:
- Assess current automation maturity: Among enterprises in the highest automation bracket, 25% had already adopted agentic AI by August 2024, and another 25% planned adoption within a year. Companies with lower automation maturity must first build that foundation.
- Identify high-impact use cases: Focus on workflows with clear ROI where agents can demonstrate measurable value
- Invest in infrastructure: Prioritize platforms with native integrations, open APIs, and flexible orchestration capabilities
- Build governance frameworks: Establish clear policies for agent behavior, human oversight, and compliance
- Develop talent: Train teams on intent-first design, multi-agent orchestration, and agentic UX principles
Conclusion: The Inevitable Transition
The shift from static buttons to agentic experiences isn’t a matter of if, but when and how. This emerging vision of an open agentic web, where AI agents make decisions and perform tasks on behalf of users or organizations, represents the next evolution of the internet.
For users, this means more natural, efficient, and powerful interactions with technology. Instead of memorizing button locations and menu structures, they’ll describe what they want to accomplish and let agents orchestrate the details.
For businesses, the stakes are existential. 73% of survey respondents agree that how they use AI agents will give them a significant competitive advantage in the coming 12 months. The companies that master agentic experiences will redefine what’s possible in their industries. Those that don’t risk becoming relics of the button-clicking era.
The agentic web is here. The only question is whether your organization will help shape it or struggle to adapt to it.
Key Takeaways
✓ Agentic experiences shift from explicit user actions to delegated intentions, fundamentally changing how we interact with digital systems
✓ The market is growing at 43.84% CAGR, with 79% of organizations already adopting AI agents in some capacity
✓ Gartner predicts 40% of enterprise apps will include agentic AI by 2026, up from less than 5% today
✓ New UX principles emphasize transparency, collaboration, and intent-first design rather than static interface elements
✓ Success requires orchestration, governance, and methodical implementation, not just deploying the latest AI models
✓ Organizations have a narrow window to develop their agentic strategy before falling behind competitors
