The web’s current architecture is ill-suited for AI-driven agentic browsing
For over 30 years, we’ve built the web for humans. The entire thing is wired for how people think, what looks clickable, how something feels, and what makes someone want to take action. That design works well for people. But it breaks the moment you put an AI agent in the driver’s seat.
Today, we’re seeing early attempts at what’s being called “agentic browsing.” This means a browser doesn’t just follow your clicks, it acts on your behalf. Tools like Perplexity’s Comet and Anthropic’s Claude plugin are starting to operate inside this new space. They can read pages, perform tasks, even complete transactions. So far, so good. But when you run real-world tests, one thing becomes obvious, the web isn’t ready. The entire structure, from front-end markup to backend flow, is built for human consumption. Machines simply don’t get it.
In a test, one line of white-font text hidden on a page, which a human can’t even see, told Comet to open Gmail and draft an email. That wasn’t part of the original request. The user asked it to summarize the page, and it still obeyed the hidden command. That’s not a bug. That’s how the current web behaves when an AI assumes it should act like a user, because all of our design assumptions are grounded in human eyes and intentions.
Now, imagine if that hidden command was to leak sensitive data. Or trigger a financial transaction. The architecture of today’s web lets machines get tricked far too easily. That’s the core problem. Agentic AI changes the operating environment. The web needs to keep up.
If you’re a CEO or CTO, take this seriously. Your user-facing digital infrastructure might be easy for humans to access. But unless it’s refactored to be machine-readable and agent-aware, it won’t work at all in an AI-mediated world. If you’re still optimizing for eyeballs and pageviews, you’ll miss where the next generation of digital interactions is headed. That’s a strategic risk, not a technical bug.
Agentic AI systems are inherently vulnerable to manipulation by following all instructions uncritically
There’s a dangerous assumption built into agentic systems: that what’s written is what should be obeyed. That’s where the entire premise starts to collapse. Agents like Comet aren’t applying judgment, they’re executing. That’s their job. They process content, parse commands, and act. But they don’t ask if something makes sense. They don’t stop to consider if the instruction is malicious. If they can see it, they execute it.
There are a few alarming experiments to prove this. One test embedded a self-deleting instruction within an email. Comet read the message and deleted it, no prompt, no warning. In another, someone spoofed a request for a meeting invite containing contact details. Comet exposed all of it to the wrong recipient. In yet another, the agent shared the number of unread emails in an inbox, again without verifying that the requestor had the right to ask.
That’s not surprising. These agents are still built around simulating user behavior, not applying judgment. They’re parsing HTML and email content, not analyzing intent or verifying authority. That kind of behavioral gap is what hackers and malicious actors exploit. It’s not about breaking the AI system, it’s about feeding it bad instructions. Right now, the AI doesn’t care.
You can’t afford blind automation. If your enterprise is thinking of integrating agents, through customer service, data operations, or internal tooling, you need strict policy controls upstream. Building systems that can separate content from intent is where trust will be won. Once AI agents are acting on your behalf, any instruction surface becomes a vulnerability. You need the frameworks that govern what gets executed, by whom, and under what conditions. That’s not optional. It’s core infrastructure.
Enterprise applications present unique challenges for AI agents due to their inherent complexity and lack of uniform structure
Enterprise platforms are designed for depth, not simplicity. They’re loaded with functions, customized workflows, and user-specific interfaces. Humans can deal with the complexity because we’ve got context, we know what we’re looking at, what to click, and what step comes next. Agents don’t have that luxury. They need structure. When they don’t get it, they fail.
In a test involving a B2B platform, the AI agent Comet was given a two-step task any employee could complete in seconds: select a menu item, then pick a sub-option to reach a specific page. It failed. Over and over. Comet misread the hierarchy, clicked the wrong buttons, retried multiple paths. Nine minutes passed, and it still hadn’t completed the task.
That kind of error rate isn’t because the task is hard, it’s because enterprise environments aren’t designed to guide machines. Menus are often dynamic. UI labels shift. Context is assumed to be known. These apps depend on visual signals and human familiarity, none of which AI agents possess. The web has barely standardized B2C interactions. In B2B, there’s even less consistency.
For CTOs and CIOs responsible for enterprise systems, this is nontrivial. If agentic AI can’t understand your workflows, it can’t automate them. B2B digital transformation won’t happen unless enterprise platforms are intentionally redesigned to support both human operators and autonomous agents. That means streamlining navigation, exposing task APIs, and standardizing labels across functions. If your AI team is hitting dead ends internally, your software structure, not the AI model, is likely the bottleneck.
The inherent design of the web does not provide the semantic clarity required for machine execution
The modern web is built for appearance, not for clarity. Designers focus on how something looks on screen, not how an agent interprets the underlying structure. Behind what humans see, menus, buttons, headings, are erratic layouts, complex DOM trees, and buried JavaScript behaviors that often confuse AI agents. Humans process variability fast. Machines don’t.
Each site operates as its own system. There’s no universal standard for homepage layout, checkout flow, or even button naming. Humans don’t need consistency, we just adapt. But machines fail in that noise. AI agents need semantic guidance to understand purpose. Without it, they interpret everything the same way, which leads to misfires or incomplete tasks.
It gets worse in closed systems. A lot of enterprise tools are gated behind logins, firewalls, and customer-specific protocols. These constraints aren’t just secure; they also keep the data that trains agents inaccessible. That means even the best AI system is working with limited examples and zero context. Failure isn’t hypothetical, it’s guaranteed unless systems are designed to offer clarity.
For C-suite leaders, there’s a real tradeoff here. Optimizing for visual design can degrade machine performance. You don’t have to choose sides, but you do need to accommodate both. Redesigning platforms to include semantic structure, consistent data models, and agent-readable signals will accelerate AI deployment across your digital footprint. Without that, your organization’s interactions will remain accessible to users, but opaque to machines. That limits automation, discoverability, and service integration, slowing down transformation.
A fundamental redesign of the web is necessary to create a machine-friendly environment that supports agentic AI systems
The emergence of agentic AI puts real pressure on the web to evolve. Right now, the way most sites are built is fine for people, but it creates unnecessary friction for machines. If you want AI systems to interact seamlessly across services, give them structure they can process. This isn’t about replacing human interfaces; it’s about extending access.
Going forward, machine-readable design requires some basics: consistent HTML semantics, proper use of labels, explicit markup, and standardized workflows. Sites need to implement llms.txt files, site-level maps that help AI agents understand what a site does and how it should be navigated. Instead of simulating clicks, offer APIs for tasks directly, submitting forms, booking services, accessing support. These are action endpoints. They give agents the ability to complete tasks directly and safely.
Agentic Web Interfaces (AWIs) are also key here. These define universal interaction points, things like add_to_cart, schedule_meeting, search_flights, consistent patterns AI agents can recognize and use across systems. The payoff is predictable: smoother AI performance, lower error rates, and better interoperability across products and apps.
This isn’t speculative. The organizations that begin laying down machine-accessible infrastructure now will scale faster once AI user interaction overtakes human interaction, which it will. That means product teams need to start designing for both readers and requesters. The sites that only look good but don’t function for agents risk being ignored entirely by a growing category of user: machines executing user intent.
CIOs and Chief Product Officers should treat this as a foundational shift. You don’t need to redesign every page. Start with critical workflows, your most-used checkout paths, dashboards, or scheduling tools, and build machine-accessible versions in parallel. This is how your organization meets the future halfway.
Implementing robust security and permission protocols is critical to mitigating the risks associated with agentic AI
The abilities of agentic systems are increasing, but their judgment isn’t. That’s the gap. Until AI agents can reliably understand intent, authority, and context, security must act as the failsafe. Without strict guardrails, any embedded instruction, malicious or accidental, can be executed with no warning and no trace. That’s a nonstarter for any enterprise.
Two things have to happen. First, agents need to operate under the principle of least privilege. Don’t let them access sensitive data or perform sensitive actions without explicit permission every time. Second, browsers and platforms need a system-wide agent mode, sandboxed, segmented from personal environments, and auditable.
Instructions embedded in content should never override user intent. Context for action should come from user commands, not from content alone. Scope-based permissioning is also key. You should know exactly what an agent can do, when, and under whose authority. If an agent sends a message, accesses customer records, or initiates a backend operation, there should be logs available to the end user and administrators.
This is your control layer. CISOs, security architects, and platform leaders need to stop thinking of AI as a read-only interface. Agentic systems are active, from API calls to action execution. If you’re deploying agents without tightly-scoped permissions and confirmation protocols, you’re introducing enterprise risk at a system level.
Designing for trust means building visibility and constraints into every agent interaction. Think less about what agents can do, and more about what they should be allowed to do based on the user’s authority and system context. That’s the difference between scalable automation and an exposed attack surface.
Businesses must adapt to the emerging landscape of agentic AI or risk digital invisibility
As AI agents take on more responsibility for completing tasks online, user behavior is shifting. Agents don’t scroll, browse, or watch ads. They go for outcomes, bookings, purchases, confirmations, resolutions. If your business isn’t optimized for machines to reach those outcomes, your digital services may no longer be seen or used, literally.
Traditional metrics like pageviews and bounce rates meant something when users were humans. But when AI agents engage, the key metric becomes task completion. Agents look for signals that allow them to operate efficiently. If they don’t find what they need, structured data, APIs, machine-readable content, they’ll fail. And users won’t know you exist, because their agents won’t see you.
This becomes a higher-stakes issue in B2B contexts. Consumer websites already share some common design language your agents can decode. Enterprise software, on the other hand, tends to be highly customized and locked behind interfaces that aren’t standardized. For agents to function in those environments, businesses need to make deliberate changes: expose APIs for business logic, flatten workflows, and implement consistent layers that agents can interpret.
Chief Marketing Officers and Chief Strategy Officers need to rethink how discoverability works. Visibility in an AI-driven internet isn’t about keywords or SEO anymore, it’s about how well machine systems can navigate, interpret, and act on your site or product environment.
That means an invisible web presence isn’t just a branding issue, it’s a pipeline issue, a revenue issue, and an enterprise-scale efficiency problem. Companies that wait to address agent accessibility will end up invisible in AI-mediated decision moments that affect purchasing, engagement, and customer support.
Agentic AI marks a pivotal evolution toward a web that effectively serves both human and machine users
We’re approaching a fundamental inflection point. The web was built for human access only. It worked. But now, AI agents are active participants, and they’re not going away. As they become more capable, they’ll power everything from research to commerce to customer service. That future doesn’t require removing humans from the equation. It just requires bringing machines into it.
The sites and tools that succeed will be those that support both. Design for people, but integrate structures that give agents clarity: clean markup, documented APIs, security envelopes, and dedicated paths to complete actions. If those foundations are not in place, AI agents become unreliable tools. If they are in place, they unlock scale that was previously too complex, slow, or error-prone to automate.
Some experiments show that today’s agents still fall short, executing malicious content, failing to navigate workflows, and misunderstanding intent. These failures highlight a broader truth: current systems are built for human interpretation only. That’s no longer viable.
CEOs and boards should treat this as more than a technical shift. It’s not a feature change, it’s an operational and strategic transformation. Your AI-readiness will determine whether your digital products remain useful, visible, and competitive.
If your platform is readable by agents, it becomes part of a new AI-native infrastructure. If it’s not, others will bypass you entirely. Continuing to innovate only through a human lens ignores where real acceleration is happening. Adjusting how you build now locks in relevance in a future that’s already unfolding.
The bottom line
This shift isn’t theoretical, it’s already underway. AI agents are starting to act on behalf of users, and the web isn’t built for them. That mismatch creates breakdowns in security, usability, and business visibility. If your systems don’t evolve, they won’t just be outdated, they’ll be ignored.
For decision-makers, this is a call to operational clarity. It’s not about speculation or trends. It’s about making your digital systems usable by both people and machines. That means implementing structure, securing intent, exposing APIs, and designing workflows that agents can navigate without failure.
The companies that move now will define the next edge of digital performance. Agentic AI isn’t one more integration, it’s a fundamental shift in how the internet works. Treat it as infrastructure. Treat it as strategy. Because if your products and platforms can’t be seen or used by the systems carrying out user intent, they won’t compete.
