How to get ready for the agentic internet and the rise of agent experience

<p>The internet is broken. Not technically—the packets still flow, and the servers still serve. But experientially, it no longer works as it should. Users navigate a maze of fragmented apps, each with separate logins, different interfaces and incompatible data. They flip between websites, compare options across tabs, reenter the same information multiple times and generally exhaust themselves with digital labor that adds no real value to their lives.</p> <p>We’ve accepted this situation as normal, but it's actually absurd. We've built an internet that exhausts rather than empowers users.</p> <p>Into this dysfunction comes a convergence of technologies that promises not just to fix these problems but to fundamentally reimagine the digital interaction itself. Natural language interfaces have finally become truly workable. AI agents can now query databases, call APIs and execute code to complete multistep tasks. While many tech companies are scrambling to use these capabilities to make the current digital experience just a bit more efficient, the real transformation lies in what we call the agentic internet.</p> <h5>From agent augmentation to agent delegation</h5> <p>The agentic internet is a foundational change at the internet level itself, touching not just how we browse but also how the entire digital infrastructure ties together. The scale rivals the actual creation of the internet, the emergence of Web 2.0 or the mobile revolution. In fact, it's broader than these because it fundamentally alters who initiates, evaluates, negotiates and executes digital interactions. For the first time in the internet's history, humans won't be the primary actors.</p> <p>This is because the agentic internet marks a progression from augmentation, where agents assist humans, to delegation, where agents act on their behalf. Rather than people browsing websites and juggling apps, they’ll delegate this work to their personal AI agents. We’ll move from comparison shopping to agents negotiating the best deal. From filling out forms to agents exchanging structured data. From managing subscriptions to agents optimizing service relationships.</p> <p>The human role will change from executor to policy setter. We will define goals, constraints and preferences, then let agents execute the tactical details autonomously.</p> <p>In shopping alone, the resulting market opportunity is staggering: <a href="https://www.cognizant.com/us/en/insights/new-minds-new-markets" target="_blank" rel="noopener noreferrer">In our recent research</a> on consumer AI, we forecast that AI-powered consumers will account for 55% of all consumer spending by 2030, which amounts to over $4 trillion in the US. By 2030, we anticipate that AI will be fully embedded in the consumer journey.</p>

<p><br> But shopping is not the only activity the agentic internet will impact; it will reshape how interactions happen and how work gets done in not just <a href="https://www.cognizant.com/us/en/insights/insights-blog/ai-retail-consumer-engagement" target="_blank" rel="noopener noreferrer">retail</a> but also in <a href="https://www.cognizant.com/us/en/insights/insights-blog/ai-in-the-healthcare-consumer-journey" target="_blank" rel="noopener noreferrer">healthcare</a>, <a href="https://www.cognizant.com/us/en/insights/insights-blog/ai-in-banking-finance-consumer-preferences" target="_blank" rel="noopener noreferrer">banking</a>, <a href="https://www.cognizant.com/us/en/insights/insights-blog/ai-consumer-goods-manufacturing" target="_blank" rel="noopener noreferrer">manufacturing</a>, <a href="https://www.cognizant.com/us/en/insights/insights-blog/ai-insurance-customer-experience" target="_blank" rel="noopener noreferrer">insurance</a>,&nbsp;<a href="https://www.cognizant.com/us/en/insights/insights-blog/ai-telecom-customer-experience" target="_blank" rel="noopener noreferrer">telecom</a>, <a href="https://www.cognizant.com/us/en/insights/insights-blog/travelers-use-of-ai-in-booking-trips" target="_blank" rel="noopener noreferrer">travel</a>, <a href="https://www.cognizant.com/us/en/insights/insights-blog/ai-media-tech-customer-experience" target="_blank" rel="noopener noreferrer">media consumption</a>, <a href="https://www.cognizant.com/us/en/insights/insights-blog/ai-in-energy-utilities-sector-consumer-adoption" target="_blank" rel="noopener noreferrer">utilities</a>, <a href="https://www.cognizant.com/us/en/insights/insights-blog/ai-life-sciences-consumer-engagement" target="_blank" rel="noopener noreferrer">life sciences</a> and every other domain that touches the digital world.</p> <h5>Agentification—Gradually, then suddenly</h5> <p>We've reached this tipping point through an unprecedented convergence of forces:</p> <ul> <li><b>Our research on consumer AI</b> shows that people who once hesitated to engage with algorithms now feel more confident in handing off certain tasks to AI agents<br> <br> </li> <li><b>Platform providers have embedded AI capabilities</b> directly into their core infrastructure<br> <br> </li> <li><b>Enterprise APIs,</b> built over a decade for system integration, have inadvertently created the technical substrate for autonomous transactions<br> <br> </li> <li><b>Connected devices</b> generate continuous streams of signals that agents can act on without human intervention</li> </ul> <p>And perhaps most importantly, the sheer volume of investment in AI and agentic research has reached a critical mass where consumer tools are being built regardless of enterprise readiness— tools that people will use and eventually bring into the workplace, forcing organizational adaptation from the bottom up.</p> <p>Business leaders recognize this. In our&nbsp;upcoming research on legacy modernization, only 17% think their existing infrastructure could support agentification. The vast majority (91%) are pursuing sweeping modernization programs to enable them to embed AI across their business.</p> <p>The work of agentification starts with building a superior agent experience (AX). This includes understanding the core principles of AX design and building the technical and organizational capabilities needed to develop a robust AX.</p> <p>Agent experience is a direct investment in a brand’s future utility and discoverability.&nbsp;Organizations that successfully engineer their services to be legible and trustworthy to AI agents will remain integral and relevant to their customers.</p>

<h4><br> The strategic imperative of agent experience</h4> <p>For the past two decades, commercial success has been closely linked to user experience (UX): the discipline of making technology easy and enjoyable for people to use. Now, a parallel discipline will become critical for business relevance: agent experience. As autonomous AI agents increasingly act as proxies for consumers and businesses, the AX needs to provide the necessary framework for designing digital services optimized for machine consumption. It ensures that a service is intelligible, reliable and efficient for the software intermediaries that will drive a majority of future transactions.</p> <p>This is not a future-casting exercise; traffic from autonomous agents is a growing component of digital activity today. Organizations that fail to cater to these non-human customers will find themselves at a significant competitive disadvantage.</p>

<p><br> At the same time, humans are diverse in their preferences and comfort levels. Some people will always prefer traditional websites and familiar interfaces. For example, even the most bullish respondents in our consumer AI research worry that AI agents may not have their best interests at heart.</p> <p>Agents, however, can cater to this diversity by creating traditional interfaces on the fly when needed. An agent can generate a classic web form for someone who prefers that interaction model while simultaneously conducting complex negotiations with other agents in the background, or dealing with a human at the right moment.</p>

<h5>The core principles of agent experience design</h5> <p>The design principles for humans and for agents are fundamentally different. Human-centered design often accommodates, and even leverages, ambiguity, serendipity and emotional connection. A consumer may enjoy browsing an ecommerce site, discovering new products and being influenced by brand storytelling. For the human user, the journey itself holds value.</p> <p>Agents, in contrast, operate on principles of logic, utility and efficiency. They require precision, completeness and programmatic clarity. An agent tasked with procuring a product does not &quot;browse&quot;; it executes a precise query based on specific parameters. It evaluates quantifiable evidence on multiple attributes, such as price, delivery time, carbon footprint and warranty terms, simultaneously and in milliseconds.&nbsp;</p>

<p><br> For an agent, the journey is a means to an end; the only metric of success is the optimal outcome. This operational distinction requires a completely different set of design principles and technical foundations.</p>

<h5>The technical foundations of a robust agent experience</h5> <p>For a service to be legible and trustworthy to an AI agent, it must be engineered with machine consumption as a primary design consideration. This is built on several distinct technical pillars:</p> <ul> <li><b>Structured and unambiguous data: </b>An agent cannot infer context from visual layout or cultural nuance. Therefore, all data exposed to an agent must be self-describing and consistent. Product specifications, measurements and labels must be programmatically clear and adhere to established schemas to eliminate ambiguity. Product descriptions and metadata will become paramount.<br> <br> </li> <li><b>Predictable and stable APIs: </b>To an<b> </b>agent, the API is the product. The API is what exposes the product’s functions and data in an agent-readable form. And the API’s specifications (endpoints, schemas, authorization, rate limits, service level agreements) govern how agents may use those capabilities. At a practical level, this means the API must be painstakingly detailed. It needs to be backward-compatible where possible, versioned with clear deprecation paths, fully documented (including schemas and examples) and explicit in its failure codes.<br> <br> A clearly stated code like &quot;Error 402.1: Insufficient funds&quot; or &quot;Error 404.2: Product out of stock&quot; allows the agent to make a subsequent logical decision, such as attempting a different payment method or sourcing the product from an alternative supplier.<br> <br> Agent experience depends on this predictability.&nbsp;Agents plan and act from the spec, not a user interface, so the entry point (the API) and its published terms together constitute the reliable surface of the product.</li> </ul>

<div>&nbsp;</div> <ul> <li><b>Support for inter-agent communication protocols:</b>&nbsp;Industry-wide initiatives are currently being developed to create a common language governing how agents exchange information, negotiate tasks, take payments and verify credentials. This will enable agents to dynamically discover and integrate with new services without human intervention.<br> <br> Examples include Anthropic’s&nbsp;<a href="https://www.anthropic.com/news/model-context-protocol">Model Context Protocol</a>&nbsp;(MCP), where MCP servers expose tools/data to agents, and Google’s&nbsp;<a href="https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/" target="_blank" rel="noopener noreferrer">Agent2Agent (A2A)</a> and <a href="https://cloud.google.com/blog/products/ai-machine-learning/announcing-agents-to-payments-ap2-protocol" target="_blank" rel="noopener noreferrer">Agent Payments (AP2)</a>&nbsp;protocols, which standardize inter-agent negotiation, messaging and transactions. Together, these protocols cover service integration and autonomous collaboration.<br> <br> By supporting these protocols, businesses ensure participation in broader, federated ecosystems instead of remaining an isolated digital destination. These protocols must be sophisticated enough to handle complex negotiation dynamics, establish trust programmatically and ensure atomic transactions that either fully complete or roll back cleanly.<br> <br> </li> <li><b>Verifiability and reversibility:</b>&nbsp;Trust is the currency of agent autonomy. But while humans build trust through experience and brand reputation, agents operate on a different principle: They do not trust; they verify.<br> <br> A service designed for agents must be verifiable through mechanisms like cryptographic proofs and third-party attestations that programmatically confirm claims about a product or service. Furthermore, the service must be reversible. When transactions fail, and they inevitably will, the ability to cleanly unwind them is paramount. Graceful failure recovery is a critical factor in determining whether an agent will risk engaging with a service again.</li> </ul>

<h4>Building the organizational capability for agent experience</h4> <p>Developing a superior AX requires new organizational structures, operational practices and roles. For example, strategists must embrace agents as primary customers, not just another channel. The technology function must support autonomous interactions at machine speed, not just human-paced interfaces. Operations must consider human intervention the exception rather than the default. Governance must ensure aligned autonomy rather than rigid control. The corporate culture must accept non-human participants as legitimate stakeholders in the business ecosystem.</p>

<h5>New operational practices</h5> <p>New operational practices will need to be designed as well. For example, a core practice for building a robust AX is the systematic use of a dedicated sandbox environment, which serves the dual purposes of resilience testing and behavioral analysis.</p> <p>To ensure resilience, teams can unleash a battery of real-world agents into the sandbox to discover how new APIs will interact with the system under various conditions. This includes stress-testing endpoints with high-frequency, programmatic requests that mimic anticipated agent behavior. This enables teams to identify breaking points and rectify architectural weaknesses before they affect live operations.</p> <p>The sandbox is also where developers model and anticipate future agent behaviors. By observing how different agents attempt to achieve goals, teams can understand their logic, identify inefficient pathways and uncover unexpected interpretations of the service.</p> <p>AX development also requires a new form of user research. Teams must analyze agent interaction logs with the same diligence that UX researchers apply to user session recordings. This involves meticulously monitoring API calls, protocol handshakes and data payloads to identify points of failure, hesitation or inefficient logic loops in an agent's journey. These findings can then be used to refine and improve the service.</p>

<h5><br> The modern agent experience team</h5> <p>Excelling at AX also requires a dedicated, cross-functional team that extends beyond traditional UX roles. Key functions include:</p> <ul> <li><b>AX strategist/lead:</b> Represents the requirements of AI agents in all strategic, design and technical discussions. This individual—who essentially owns the AX roadmap—would typically come from product management and partner closely with marketing and customer success to translate the roadmap into real usage.<br> <br> </li> <li><b>API product manager:</b> Manages the design, lifecycle and documentation of the machine-facing interfaces, treating them as core products. This role would sit in product development but would also require a strong software engineering background and would closely collaborate with platform teams.<br> <br> </li> <li><b>Data governance specialist:</b> Ensures that all data exposed via APIs is consistent, well documented and structured for machine readability across the organization. This role would emerge from the data management or compliance functions and coordinate with security and analytics to enforce standards.<br> <br> </li> <li><b>Agent trust and compliance lead:</b> Ensures agent interactions remain in-bounds with continuously evolving industry and AI regulations. The person in this role will also need to monitor and test public acceptance of the activities that agents handle and how they behave over time, including explaining AI-driven actions and decisions to internal and external stakeholders. Typical backgrounds for this role might include responsible AI, legal and policy management, with strong communications ties for stakeholder transparency.<br> <br> </li> <li><b>Automation and simulation engineers:</b> Design and operate the agent sandbox, building simulated agents and defining test cases that provide actionable feedback to development teams. These team members would generally be drawn from QA/test engineering and reliability engineering, working closely with software engineering to operationalize feedback loops.</li> </ul> <p>This team's purpose is to embed AX principles directly into the development and release lifecycle, establishing quality gates for API stability and data clarity alongside existing checks for security and performance.&nbsp;</p>

<h4><br> A look at agentification across industries</h4> <p>The agentic internet won’t affect all industries equally or simultaneously. In our industry-by-industry analysis of consumer AI adoption, low-stakes retail and grocery shopping sits well above the average on our index of consumer inclination to use AI—34 points higher than the mean. By contrast, healthcare services, such as condition diagnosis, sit 14 points below the global average, slowed by regulation and consumer squeamishness.</p> <p>This means some businesses must accelerate their AX journey today to avoid rapid disintermediation, while others have a longer runway to adapt. Across all industries, we’ll see dominant patterns emerge that will accelerate adoption and reduce implementation risk.</p>

<h5>Fast-track industries</h5> <p><a href="https://www.cognizant.com/us/en/insights/insights-blog/ai-retail-consumer-engagement" target="_blank"><b>Retail</b></a><b>:</b> Between its existing digital infrastructure and consumers’ comfort with e-commerce, retail is positioned to be an early adopter and proving ground for agent-mediated commerce. The dominant pattern here will be request-for-outcome commerce. Instead of browsing products, consumer agents will broadcast needs: &quot;outfit for business presentation in warm climate&quot; or &quot;week of meals for family of four under $150.&quot;</p> <p>Retailers will need to respond with complete solutions, not individual items. This requires a fundamental change from product-centric to outcome-centric thinking. Success metrics will need to move from traditional measures like page views and conversion rates to possible new indicators like agent inclusion score (how often agents consider your offerings) and autonomous conversion rate (how often agent negotiations result in transactions).</p>

<p><a href="https://www.cognizant.com/us/en/insights/insights-blog/ai-media-tech-customer-experience" target="_blank" rel="noopener noreferrer"><b><br> Media and tech</b></a><b>:</b> Given its relatively high consumer digital maturity and fewer regulatory hurdles, the media and tech industry will also be among the earliest sectors to see large-scale agentic transformation. In our research, consumer inclination to use AI in this sector is exceptionally high, consistently surpassing the global cross-industry average in every phase of the purchase journey.</p> <p>The likely pattern here is subscription orchestration. Rather than consumers juggling dozens of services, agents could consolidate, bundle and optimize them in the background. An agent might renegotiate a household’s connectivity plan, pause unused subscriptions and surface a content bundle aligned with viewing habits.</p> <p>Media and tech companies will need to move from channel-based competition toward ecosystem-based cooperation, where value lies in being continuously included in an agent’s optimized bundle. Traditional measures such as churn and average revenue per user may be complemented, or even displaced, by indicators like a bundle inclusion score (how often an agent selects a provider’s services in a package) and continuity rate (how long services remain part of an agent-managed plan).</p>

<h5>Slower-moving industries</h5> <p><a href="https://www.cognizant.com/us/en/insights/insights-blog/ai-insurance-customer-experience" target="_blank"><b>Insurance</b></a><b>: </b>Consumers exhibit a lower-than-average inclination to use AI for insurance services. Trust and transparency concerns suggest that adoption will be slower, paced by regulatory change and the need to build confidence in algorithmic decision-making.</p> <p>The scenario we’ll see is a move toward continuous underwriting, where risk assessment becomes real-time and dynamic. Connected devices will continuously update risk profiles, and behavioral patterns will trigger rate adjustments. Life events will automatically modify coverage, and every product will become programmable, with terms and conditions expressed as executable code rather than legal prose. Smart contracts will enforce themselves, and parametric insurance will pay out automatically when conditions are met. The entire financial stack will become responsive to real-world events without human intervention.</p> <p><a href="https://www.cognizant.com/us/en/insights/insights-blog/ai-in-the-healthcare-consumer-journey" target="_blank"><b>Healthcare</b></a><b>:</b> Healthcare saw some of the lowest AI inclination scores among consumers, reflecting heavy regulation and consumer caution. While the technical potential is enormous, mainstream transformation will lag retail and other consumer sectors.</p> <p>The pattern that will emerge is the rise of device-to-service orchestration, where medical devices will directly coordinate care. Glucose monitors won't just alert patients to check their blood sugar; they will schedule appointments, adjust medication orders and coordinate with nutrition services. Heart monitors will record data, trigger emergency responses, schedule follow-ups and negotiate with insurance providers. This will require complex consent hierarchies for sensitive data and care pathways expressed as executable workflows; however, it will enable a level of proactive care that was previously impossible.</p>

<h4>The gateway to the agentic internet</h4> <p>In a world where agents drive the majority of digital interactions, businesses that aren’t visible to agents will become economically irrelevant. Superior products, competitive prices and excellent customer service—none of this will matter if agents can't find, evaluate and transact with you.</p> <p>The rewards justify the radical changes required. As agents efficiently match needs with offerings, organizations will see dramatically lower customer acquisition costs. Transaction velocity and volume will increase as friction disappears from the buying process. Customer relationships will deepen through continuous engagement rather than episodic transactions. New revenue streams will emerge from agent-specific services and capabilities. As agents learn to work with your systems, powerful network effects will create lasting competitive moats.</p> <p>The question is not whether to engage with the agentic internet but how quickly you can establish your position within it. Those who move decisively now—building the capabilities, partnerships and infrastructure for agentic interaction—will set the rules for others to follow.</p> <p>The age of human-only digital interactions is ending. The agentic internet has begun. The only question is whether you'll help design it or be designed out of it.</p>