The Agentic Web: How Autonomous AI Agents Could Reshape the Internet’s Next Era

The internet is on the cusp of a new transformation. After the static pages of the PC Web and the mobile app explosion of the last decade, a third era is emerging – one defined not by humans clicking links or scrolling feeds, but by autonomous AI agents acting on our behalf. Researchers are calling this next evolution the Agentic Web, an internet of goal-driven digital agents that perceive, reason, and act to fulfill users’ intentions . In this vision, your future browser might be less a window for manual surfing and more an “agent browser” – a proactive partner that takes high-level requests (“Plan my trip to Tokyo”) and executes a complex chain of actions to deliver results . It’s a paradigm shift that promises to redefine how we interact with technology, moving from direct manipulation of websites and apps to delegated autonomy and orchestration by intelligent software.

This article is a deep dive into the Agentic Web – what it is, the technology stack enabling it, and how it differs from the web eras that came before. We’ll explore the three pillars of this new web (Intelligence, Interaction, and Economy), peek at real-world applications already emerging, and examine the daunting technical and societal challenges that come with weaving a web of AI agents. We’ll also see how this vision overlaps with and diverges from other future-internet concepts like Web3’s decentralized web. The goal is to paint a comprehensive picture of the Agentic Web’s architecture and implications, in a way that’s accessible to developers and tech enthusiasts who are watching the web’s evolution with equal parts excitement and caution.

From PC to Mobile to Agentic: The Web’s Evolution

The Agentic Web doesn’t arise in isolation – it’s the next chapter in an ongoing evolution. To understand its significance, it helps to compare it with the PC Web era that started it all and the Mobile/Web 2.0 era that dominates today . Each phase of the web has been defined by how users interact, how information is structured, and how value is generated online. The table below summarizes these shifts:

Web Era	Dominant User Role	Interaction Model	Economic Focus
PC Web (1990s–2000s)	“Navigator” – users manually browse and search	Point-and-click, text queries (desktop browsers)	Search-centric: static pages, hyperlinks, search ads targeting user queries .
Mobile Web (2010s)	“Operator” – users tap and scroll through curated apps/feeds	Touch interfaces, mobile apps, algorithmic feeds	Attention Economy: personalized content streams and targeted ads competing for human attention .
Agentic Web (Emerging)	“Director” – users delegate goals to AI agents (“do this for me”)	Conversational and autonomous agent interactions	Agent Attention Economy: services compete to be invoked by user’s agents rather than clicked by users ; value created via agent-to-agent services and outcomes.

In the PC Web era, exemplified by the early World Wide Web, the user was firmly in control of navigation. You had to actively search for information (think Yahoo! or Google search results) and click through static pages of content. Information was hyperlinked, but essentially user-driven and manual. Commercially, this era introduced models like search advertising (e.g. Google AdWords) that monetized the simple search-query-to-result flow .

The Mobile Web era (broadly the 2010s and early 2020s) shifted interactions to smartphones and apps, bringing a far more dynamic and personalized experience. Instead of manually querying everything, users were fed algorithmically curated content – the rise of social feeds, push notifications, and recommender systems. The smartphone made the web ubiquitous and always-on, and businesses learned to compete in the attention economy, vying for every extra second of your screen time . Recommender algorithms and engagement-optimized feeds became the new power brokers of the web’s economy .

Now enters the Agentic Web era, which researchers argue is starting now and could fundamentally upend those paradigms . In this emerging model, users step back from constant point-and-click or touch interactions. Instead, we delegate tasks to AI-driven agents that can act across the web on our behalf. Imagine telling an AI assistant, “Plan a 3-day trip to Beijing,” and it autonomously searches flights and hotels, checks weather forecasts, maps out sightseeing routes, and presents you a complete itinerary – all while you’re hands-off . The underlying shift is from a web designed primarily for human consumption to a web where machine agents are first-class actors.

Crucially, this means who competes for what in the digital economy also changes. In an agent-driven world, it’s not just about grabbing eyeballs with clickbait or ads; it’s about being the service or API that a user’s agent chooses to fulfill a task. Researchers dub this the “Agent Attention Economy,” analogous to the human attention economy of Web 2.0 . Just as websites once fought for user clicks, now third-party tools and services will compete to be invoked by agents. For example, multiple travel-booking services might offer APIs, but only the one that an AI travel agent deems most suitable gets the “business” – possibly without a human ever directly seeing the alternatives. This introduces new incentives for web services to structure their data and APIs in ways that are agent-friendly and to advertise their capabilities in machine-readable form.

Before diving deeper, it’s worth noting that these eras aren’t strictly sequential with hard cut-offs. Elements of the PC and Mobile web persist and blend. But the Agentic Web represents a qualitative leap – adding an autonomy layer on top of the web’s fabric. The research paper “Agentic Web: Weaving the Next Web with AI Agents” (Yang et al., 2025) frames this as moving the internet from a “passive repository of information” to a “dynamic environment of action,” populated by autonomous systems that plan and execute goals . It’s a continuation of the web’s evolution, but one that could feel as revolutionary as the jump from desktop web to the smartphone app world.

Meet the Agentic Web: Agents as Users and Interfaces

So, what exactly is an “agent” in this context, and how does the Agentic Web function? In simple terms, an agent is a software entity (often powered by AI, especially large language models) that can autonomously perform tasks in the digital world. These tasks might range from retrieving information, to scheduling and purchasing, to collaborating with other agents or services. The defining feature is that agents operate with a degree of goal-directed autonomy – they don’t just react to a single query and stop, they can carry out multi-step plans and make decisions along the way to achieve a user’s objective .

One way to understand the Agentic Web is through two complementary roles that AI agents play in this ecosystem :

Agent-as-User (downward-facing): In this role, an AI agent behaves much like a human user on the web. It can browse websites, click links or buttons (via APIs or scripted interactions), fill out forms, read and parse content – all autonomously. Essentially, the agent navigates and consumes web resources on behalf of a human. Research prototypes have already shown agents that can, for example, read web documentation and perform software installation steps, or scrape data from e-commerce sites as a human would, but at machine speed and 24/7 . This “agent as a web user” means that existing web interfaces built for humans could be utilized by agents (though not always efficiently – hence new protocols, which we’ll get to). It augments or replaces the human in routine browsing and transactional tasks.
Agent-as-Interface (upward-facing): Here, the AI agent serves as an intelligent intermediary between a human user and the web. Instead of you having to know which website or app to use and how, you interact with a personal agent (say via natural language). The agent translates your high-level instruction into concrete steps, orchestrating across multiple services as needed . In this mode, the agent presents itself as your assistant or the front-end for accomplishing things. For example, if you tell a future digital assistant “I need to refinance my mortgage,” the agent might consult bank APIs, do rate comparisons, gather forms, and come back to you with the best options – all through a conversational interface. The user experience is one of conversational delegationinstead of manual app-by-app labor.

These two roles aren’t mutually exclusive; in fact, a single agent often embodies both at once . Your agent can talk to you in natural language (agent-as-interface) while simultaneously browsing and transacting online (agent-as-user) behind the scenes. In effect, the agent becomes a bidirectional bridge – upward to the human, abstracting complexity into a friendly dialogue, and downward to the web, executing tasks in the wild digital ecosystem .

This marks a profound shift in who or what is driving interactions. In the traditional model, a web browser is a passive tool, waiting for a user’s clicks and keystrokes to decide what to do next. In the Agentic Web model, we instead get an “agent browser” that actively drives the process, taking your objective and running with it autonomously . As one research section put it, “traditional browsers function as passive, user-driven tools… In stark contrast, an agent browser operates as a proactive, goal-oriented partner,” shifting the user’s role from hands-on operator to “strategic delegator.” . This has enormous implications for interface design, user trust, and control – how do you let the user oversee or intervene in an autonomous process without defeating its purpose? These are questions researchers are actively grappling with .

But before tackling challenges, let’s look under the hood at the technological architecture enabling the Agentic Web. What makes this new mode of web interaction possible now? It turns out to be a convergence of advances – especially in AI (large language models), but also in new communication protocols, frameworks for orchestrating complex tasks, and evolving system design to accommodate autonomous agents.

Under the Hood: The Technological Stack of the Agentic Web

Enabling an internet of intelligent agents requires a stack of technologies working in concert. The core building blocks include advanced AI models (to give agents “brains”), new agent communication protocols (to let them talk to services and each other), orchestration frameworks (to coordinate complex workflows), and a rethinking of web system architecture to integrate these agents. Let’s break down the key components:

Large Language Model Agents as “Brains”: Modern AI agents are often powered by large language models (LLMs) like GPT-4, Anthropic’s Claude, Google’s PaLM, etc. These models bring the intelligence – they can understand instructions in natural language, reason about how to solve a task, and even generate code or text to execute steps. The leap in capability from earlier AI to today’s LLMs is what makes the Agentic Web concept feasible. A decade ago, a “bot” on the web could maybe follow scripted rules; today, an LLM-based agent can dynamically figure out how to use an unfamiliar interface by reading documentation or adapt its strategy when facing an error. This cognitive flexibility (often enhanced with prompt techniques like chain-of-thought, or frameworks like ReAct that intermix reasoning and acting ) is crucial. Agents can leverage both internal knowledge (the training data and reasoning skills of the model) and external tools/resources via APIs . For example, if an agent’s model doesn’t know the latest info, it can detect that need and call a web search or a database query. This is a shift from “passive retrieval” (the old search paradigm) to “proactive information use,” where an agent decides what it needs and when to fetch it during its task . Key LLM-agent capabilities include things like long-horizon planning (formulating multi-step plans and adjusting them on the fly ), contextual understanding of diverse inputs (web text, forms, APIs), and even self-monitoring or reflection to avoid mistakes . Research is rapidly improving these capabilities (e.g. specialized planner-executor model architectures ), because a smarter agent is a safer and more effective agent.
Agent Communication Protocols (MCP and A2A): If LLMs give agents brains, protocols give them a common language to speak – both with traditional web services and with each other. The web we use today runs on protocols like HTTP for client-server communication. But an autonomous agent has needs that vanilla HTTP isn’t well-suited for. For instance, agents may maintain long dialogs with a service (not just single request-response), they may negotiate capabilities or stream updates, and multiple agents may coordinate on a task asynchronously. Enter new open protocols like Anthropic’s Model Context Protocol (MCP) and Google’s Agent-to-Agent (A2A) protocol, which have emerged as leading candidates to standardize how agents communicate in this new web . What are these exactly? MCP is essentially a protocol for an agent (backed by an LLM) to connect with external tools, data sources, and services in a structured way . It allows an agent to discover what an API or database can do, have a multi-turn “conversation” with that resource, and invoke operations with proper formatting . In effect, MCP is like giving the agent a standard “plug” (think USB-C) to access any tool that supports it . For example, if an agent needs to generate an image, it could use MCP to find an image generation service, negotiate the parameters, and get the result, all in a coherent session. Unlike stateless API calls, MCP keeps a persistent contextwith a tool, so the agent can have a back-and-forth (say refining a query) without losing state . A2A, on the other hand, is about agents talking to other agents. Launched by Google in April 2025 as an open spec, the Agent-to-Agent protocol defines how autonomous agents can message each other securely and in an organized task-centric way . It introduces structured message formats where each message is tied to a specific Task ID and context, enabling multiple agents to collaborate on complex workflows without confusion . A2A emphasizes context management, asynchronous updates, and multi-turn dialogues between agents . For instance, if your shopping agent spawns a few specialist agents (one to scour coupons, one to compare reviews, etc.), they can all exchange information via A2A, keep track of sub-tasks, and update the main agent as they progress – all with a common understanding of task IDs, state, and context . Compared to MCP’s focus on agent-to-tool interactions, A2A is focused on agent-to-agent orchestration, with features that tightly couple messages to tasks and enable coordinated teamwork among agents . Together, these protocols are seen as foundational plumbing for the Agentic Web. They aim to do for AI agents what HTTP did for web pages – define a universal way to communicate. A recent Microsoft Build presentation put it succinctly: “Open protocols like A2A and MCP are key to enabling the agentic web,” integrating these standards so agents from different vendors or platforms can interoperate by design . In fact, Microsoft announced first-party support for MCP across a slew of its products (GitHub Copilot, Azure AI, Windows, etc.), calling MCP the “USB-C of AI agents.” Such industry backing hints that these protocols (and similar efforts like the Linux Foundation’s ACP ) will likely form the backbone of an open agentic ecosystem, much as HTTP and TCP/IP did for the traditional web.
Agent Orchestration Frameworks and Architectures: Even with smart models and communication pipes, making multiple agents and services work together seamlessly is non-trivial. If you peer behind the curtain of an agentic system, you’ll find an orchestration layer that handles things like breaking high-level goals into subtasks, spawning the right agents or tool calls, and merging results. In our earlier travel itinerary example, there was an implicit orchestration: one part of the system parsed the user request, another part split the job into four sub-tasks (get weather, attractions, hotels, routes), and then results were synthesized back together . The research paper describes this as a “Client–Agent–Server” model replacing the traditional Client–Server web architecture . In this model, the Agent layer sits in the middle: on one side interfacing with users/clients, on the other side interfacing with a myriad of backend services. To manage complex workflows, specialized components are proposed: for example, a “task router” that dispatches tasks to appropriate agents (potentially across a distributed network of agents, even leveraging edge computing for latency) . Another component is a “demand skill mapper”, essentially translating an application’s needs or a user’s request into a structured format that agents and tools can understand (using embeddings or ontologies to match tasks with capabilities) . And notably, they even suggest a cross-agent billing ledger to track resource usage and costs when multiple services and agents are involved – more on that in the economy section. We’re also seeing the rise of high-level agent development frameworks (both in open source and from companies) that simplify orchestrating agents. Microsoft’s Azure AI Foundry, for instance, introduced an Agent Service that lets developers compose “multiple specialized agents to handle complex tasks,” providing an SDK that integrates patterns like plan-and-act and tools for observation/tracing . Under the hood, that service is integrating A2A and MCP for communication . Open-source efforts like LangChain or Haystack have earlier provided the means to chain LLM calls with tools, but more robust frameworks are evolving to handle concurrency, long-running tasks, and reliability. Researchers are actively exploring how to architecturally separate planning from execution (so one model plans the overall strategy and another executes step-by-step, which has shown improvements on long tasks ), and how to ensure an agent can dynamically replan when something unexpected happens mid-task . In short, an Agentic Web system is far more complex than a single user sending a single request to a server – it’s more like an orchestra of models and services, and getting them to play in tune requires new kinds of conductors.
Semantic and Structured Data Interoperability: Another piece worth mentioning is how information on the web might need to be exposed in more agent-consumable formats. Agents thrive on structured data and clear semantics. Traditional web pages built for human eyes (full of unstructured text or images) can be parsed by AI to some extent, but it’s error-prone. The Agentic Web will accelerate trends like web APIs for everything, data schemas, knowledge graphs, and maybe even new markup standards that allow agents to easily understand what a page or service offers. The introduction of protocols (MCP, A2A) already pushes services in that direction: for example, under MCP an external tool is expected to provide a capability description that an agent can fetch, essentially a manifest of “here’s what I can do and how to call me” . This is reminiscent of how websites provided sitemaps for search engines; now they may provide “agent cards” or JSON manifests for agent consumption. In fact, an example in the A2A context describes an agent retrieving “JSON-formatted Agent Cards from a public URL to identify remote agents whose capabilities match the task” . It’s easy to imagine an “Agent Yellow Pages” emerging – directories or registries where agents advertise their capabilities and find each other (the Linux Foundation’s Agent Communication Protocol (ACP) effort is exploring a form of this for discovery and interoperability ). In essence, the Agentic Web will require the web’s data to be machine-friendly by default, not as an afterthought. Some of this will ride on existing standards (e.g. JSON-LD, schema.org metadata, GraphQL endpoints), and some might be new standards specifically for agent discovery and negotiation.

All of these elements – smart LLMs, open protocols (MCP/A2A), orchestration frameworks, semantic data layers – form the infrastructure of the Agentic Web. It’s akin to a stack: at the bottom, network and cloud resources to run these agents; above that, protocols letting them communicate; then the agent reasoning models themselves; and at the top, applications that bring agents to end-users in useful ways. With this foundation laid, let’s examine the conceptual model researchers use to frame the Agentic Web’s capabilities: a three-dimensional framework spanning Intelligence, Interaction, and Economy.

Three Dimensions of the Agentic Web: Intelligence, Interaction, Economy

Yang et al. (2025) propose that the Agentic Web can be understood along three key dimensions, each representing a critical aspect where this new paradigm departs from the prior web eras . These are the Intelligence Dimension, the Interaction Dimension, and the Economic Dimension. Think of them as layers: intelligence (AI capabilities) enables richer forms of interaction, and together these enable new economic structures in the web ecosystem . Let’s unpack each in turn:

Intelligence: From Static Content to Autonomous Reasoning

The Intelligence Dimension is about what kind of “smarts” an agent needs to operate autonomously online . In the traditional web, “intelligence” was mostly on the user side (human brains interpreting information) or confined to narrow AI like search ranking algorithms. In the Agentic Web, the agents themselves carry a substantial cognitive load. They must understand a wide variety of inputs and make complex decisions continuously without a human guiding every step .

Key capabilities under this dimension include:

Language and Context Comprehension: Agents should parse not just text but also semi-structured data, UIs, API docs, etc., and do so in context. For example, an agent reading a travel site needs to extract prices, locations, availability – essentially doing information extraction on the fly. Unlike a keyword search, the agent might have to understand a vendor’s webpage structure or a PDF form to get what it needs . Advances in NLP and multimodal models are enabling this – today’s agents can use vision-language models to “see” a webpage screenshot, or employ OCR to read PDFs, integrating multiple modalities.
Long-Horizon and Adaptive Planning: Unlike a single query response, many goals require multi-step strategies. Agents need to plan several moves ahead (e.g., “to get a refund I first need to find the order number, which means logging into email to find the receipt…”). The ability to formulate, execute, and revise plans is crucial . Research like the Plan-and-Act framework explicitly separates a high-level planner model from a low-level executor model to improve this, allowing dynamic re-planning after each step . In practical terms, an agent should adjust if, say, a service it tried is down – it might switch to an alternative or try again later, rather than just failing.
Learning and Adaptation: Agents won’t be very “agentic” if they are static. They ought to learn from experience, adapting to a user’s preferences or new information over time . For example, a shopping agent might notice you always prefer eco-friendly options and incorporate that into its decision-making. This could involve online learning (updating some internal state or fine-tuning on the fly) or more simply remembering past interactions (persistent memory). The intelligence dimension thus contemplates persistent agent state – unlike a stateless web search, an agent may carry context across sessions, which is both powerful and potentially risky (as we’ll see in security concerns).
Self-monitoring and Correction: To be reliable, agents should have a degree of metacognition – i.e., the ability to monitor their own reasoning and catch errors or anomalies. This is where techniques like chain-of-thought prompting (where the model explains its reasoning) and validation steps come in. If an agent is booking travel and it “thinks” through the steps, it might double-check that a flight and hotel match the same date range, catching a possible mistake. The authors highlight that agents should “monitor and reflect on their own reasoning, detect failures or suboptimal behavior, and adjust their strategies” . This is analogous to a human stepping back and double-checking their work, but in an automated fashion.
Multi-modal Integration: Many tasks involve more than text – think charts, maps, images, etc. Agents will need to draw on vision, speech, and other modalities in addition to text. For instance, a home repair agent might analyze a photo of a broken appliance to identify a part to order. Multi-modal AI models are making this increasingly feasible . In web terms, an agent might consume a mix of API data, HTML pages, and visual content to accomplish a goal.

In summary, the Intelligence dimension is what moves us from an internet that simply serves data to humans, to an internet where machine agents actively interpret and use data on their own. It’s the difference between an AI that just answers a question when asked (generative AI) and an AI that figures out what questions to ask and which actions to take next (agentic AI) . Without sufficient intelligence, an agent would be a brittle script; with it, the agent becomes a flexible problem-solver – essentially an autonomous mini-CEO for each task.

Interaction: New Protocols and Agent-to-Agent Collaboration

The Interaction Dimension covers how agents engage with the digital environment and with each other . This is where those new protocols and frameworks come into play, transforming the web from a human-mediated interaction space to a machine-native interaction space. Several shifts define this dimension:

From Hyperlinks to Semantic, Dynamic Links: Traditionally, the web’s interactions are through hyperlinks clicked by users, and APIs called by predetermined scripts. In the Agentic Web, agents don’t need a human to click links; they discover relevant resources through more dynamic means, like querying a service registry or using an API description. Interaction becomes semantic – an agent might ask “who can provide weather data?” and find a service, rather than following a static link. There’s a move toward runtime service discovery, where agents flexibly find and connect to what they need on the fly . This is supported by protocols that allow capability discovery. For example, MCP provides “dynamic capability discovery” – an agent can ask a service what functions it offers, which is far more flexible than a fixed API contract . The net effect is that interactions are no longer hard-coded; they’re figured out at runtime via richer protocols.
Persistent, Dialogic Exchanges vs. One-off Calls: Human web interactions often involve sessions (you log in, you have a session cookie), but much of machine communication (REST APIs, etc.) has been stateless. Agents, however, often benefit from maintaining a dialogue with services – keeping context between calls. MCP, as noted, enables persistent, coherent dialogues between an agent (LLM host) and a service . For instance, an agent could start a session with a cloud storage service, list files, refine the query, download something, all in one contextual thread. This is more efficient and powerful than repeated isolated API calls, and it mirrors how a human might interact (e.g., having a multi-turn conversation with a customer support chatbot – here the agent is the one conversing with an API in a stateful way). A2A similarly treats multi-turn agent conversations as first-class, with context identifiers ensuring continuity .
Multi-Agent Coordination: A crucial aspect of interaction is that it’s many-to-many, not just one agent talking to one server. Agents will talk to other agents to coordinate. We see early signs of this in projects like AutoGPT (an experimental open-source agent that could spawn sub-agents) and more polished implementations in enterprise software where, for example, a sales agent bot might coordinate with a finance agent bot to close a deal. The A2A protocol explicitly focuses on heterogeneous agent collaboration, ensuring that context and tasks are shared consistently among a team of agents working together . This means an agent can delegate to others – just as a human manager delegates tasks to a team. For the web, this could enable scenarios like an agent breaking a complex job into parts: one agent handles data gathering, another handles number-crunching, another handles interfacing with a legacy system, and so on, then they merge results. Multi-agent systems are not new in computer science, but standardizing how they communicate (A2A) and tying it into the web context is new.
Agents as Services: Another facet is that agents can themselves expose interfaces to other agents. For example, you might have a personal agent that knows your preferences; a travel agent AI might query your personal agent for preferences while planning a trip. In effect, agents can provide APIs to others. This begins to blur the line between what is a “service” and what is an “agent.” In the Agentic Web, any autonomous entity could potentially be both a consumer and provider of services. This is a shift to a more peer-to-peer model of interaction, albeit orchestrated through structured protocols. It hearkens to some visions of a decentralized web (where every user is also a server), though here it’s every agent can be a service endpoint. Efforts like the aforementioned agent registries or directories would support this by letting an agent announce, for example, “I can handle scheduling tasks” so that other agents can find and call it.

The Interaction dimension is basically about creating the communication substrate for all these autonomous transactions. It’s like designing the roads and traffic laws for a new kind of traffic (AI-driven cars on the road of the internet, to stretch a metaphor). Without it, we’d have a Wild West of bots scraping HTML and brittle hacks. With proper protocols and standards, we move towards a more organized, interoperable agent ecosystem. Microsoft’s endorsement of open agentic protocols at Build 2025 underscores how critical this layer is – they even introduced features like Azure “Agent ID” (a sort of identity and credential system for agents) to manage and secure these interactions . That hints at one more challenge: making sure interactions are secure and governed, which we’ll revisit later.

Economy: Autonomous Services and the “Agent Attention” Shift

Finally, the Economic Dimension addresses how the web’s business and value dynamics change when autonomous agents become key players . In prior eras, economic value on the web was mostly generated through human attention and transactions initiated by people. In the Agentic Web, we anticipate machine-driven economic activity – agents themselves initiating transactions, negotiating deals, or producing content/value for other agents or users.

Several important points in this dimension:

Agents as Economic Actors: Once agents can initiate transactions without direct human input, they essentially become economic agents in their own right . This could mean an agent can purchase services or goods on your behalf (with a preset spending limit or criteria). For example, your home IoT agent might automatically reorder groceries or negotiate energy purchases. It also means agents might enter into contracts – perhaps in the form of smart contracts on a blockchain (some researchers are already looking at connecting agents with blockchain for trust and automation ). The Agentic Web could see machine-native markets, where agents pay other agents for information or services. Imagine an agent running a research task paying small amounts to various data providers to get the info it needs, all decided autonomously. This is a step beyond our current ad-based model or subscription model, introducing the notion of autonomous economic decisions.
Agent-Driven Value Creation: Agents don’t just consume; they can produce outputs that are valuable. The paper notes that agents can generate artifacts like executable workflows, tool manifests, or datasets specifically for other agents to use . In other words, agents might create things that aren’t even intended for humans, but improve the machine ecosystem. For example, one agent could summarize a lengthy document in a structured way that other agents can ingest easily – effectively one agent creating a dataset for the benefit of others. This is akin to how humans produce content for others to consume, but here it’s closed-loop: agents producing for agents. Such “machine-oriented artifacts enable closed-loop systems of generation and consumption,” leading to a self-sustaining cycle of autonomous value creation . It’s a fascinating idea: parts of the web might have agents continuously cooperating and transacting with each other with minimal human oversight, optimizing and refining their outputs.
Decentralized Autonomous Economies: When you have many agents transacting, you get what looks like a digital economy of bots. Some foresee something like decentralized economic networks where agents discover services, negotiate terms, and optimize outcomes algorithmically . These could operate at speeds and scales beyond human capability – microtransactions in milliseconds, dynamic pricing adjustments, etc., all handled by AI. This links to ideas in decentralized finance (DeFi) and Web3. In fact, the convergence of Web3 and agentic AI is already in progress – e.g., projects like ChainOpera have demonstrated combining blockchain smart contracts with AI agents, allowing agents to interact in a trustless environment and even autonomously trade digital assets . The overlap between the Agentic Web and Web3’s decentralized web is particularly evident here: blockchain can provide a trust and settlement layer for autonomous agents that don’t inherently trust each other. That could enable new forms of autonomous organizations or marketplaces run by AI agents (DAO meets AI). However, unlike the pure Web3 vision, the Agentic Web isn’t inherently tied to blockchain – many agent economies could run on traditional databases and centralized systems too. The key is that the decision-making is autonomous; the infrastructure could be centralized or decentralized depending on the design.
The Agent Attention Economy (Revisited): On the commercial side, one immediate disruption is how digital advertising and marketing might change. If your AI assistant is filtering all information and deciding which options to present to you, then companies will need to convince your agent as much as they currently try to convince you. This is the notion of the Agent Attention Economy mentioned earlier – third-party services compete for agent invocations rather than direct human clicks . Practically, this might mean new forms of SEO (trying to get agents to rank or select a service) or even paying agents for priority (which raises ethical questions). It could also invert the ad model: maybe your agent gets paid micro-amounts to pay attention to marketing on your behalf. We don’t know yet, but clearly if a large portion of user interactions are intermediated by agents, companies will adapt by targeting those agents. The paper notes that “new protocols like the MCP are enabling agents to dynamically compose and orchestrate services from a modular ecosystem, leading to the development of agent-driven commercial models.” In other words, rather than a user choosing a service and seeing ads, an agent might assemble a solution from many services, potentially engaging in on-the-fly price or quality comparisons. This could commoditize certain services (e.g., if your agent always picks the cheapest API for weather data that meets the criteria, weather providers might compete primarily on being agent-affordable and reliable, not on branding).
Economic & Governance Challenges: With agents making economic decisions, we face new questions of liability and governance. If an AI agent overspends your money or makes a bad trade, who is responsible? If agents collude (intentionally or unintentionally) could they rig markets? The research highlights concerns around accountability, transparency, and alignment of agents involved in high-stakes decisions . Regulators may need to set rules for autonomous agent transactions, and users will demand transparency – e.g., “did my agent get a kickback for choosing this product?” It also raises the need for audit trails of agent decisions, and possibly for constraints like spending caps or required human approval above certain thresholds. Microsoft’s introduction of tools like Entra Agent ID (for enterprise identity) and their emphasis on governance in Azure AI Foundry suggests that industry is already thinking about preventing “agent sprawl” and ensuring oversight as companies deploy these autonomous agents .

In summary, the Economic dimension of the Agentic Web envisions a shift from an internet economy centered on human attention and centralized platforms to one where autonomous agents drive value creation and exchange. It could unlock immense productivity – imagine businesses where mundane processes are entirely handled by fleets of agents, freeing humans for creative work. But it could also cause disruption: certain jobs may be displaced, new inequalities could emerge between those who have advanced agents and those who don’t, and the ripple effects on labor and markets are hard to predict . As with any powerful technology, maximizing benefits while mitigating downsides will be key.

Having covered the architecture and core paradigms of the Agentic Web, let’s look at how this is starting to materialize in practice and what challenges lie ahead in building this vision out.

Agents in Action: Early Applications and Use Cases

Although the Agentic Web is still in its infancy, we’re already seeing sprouts of real-world applications that hint at its potential. Tech companies and open-source projects alike have begun weaving agentic concepts into products. Here are a few notable areas where agent-based systems are emerging:

1. Personal AI Assistants as Universal Interfaces: Perhaps the most visible early step toward the Agentic Web is the rise of more capable personal assistants – think beyond Siri/Alexa, towards assistants that can truly handle multi-step tasks across apps. Both Microsoft and Google are racing in this direction. Microsoft, for example, has integrated Windows Copilot into Windows 11, envisioning it not just as a chatbot, but as an orchestrator that can take complex user requests and perform actions across multiple apps or services. At Build 2025, Microsoft showcased Copilot Studio’s multi-agent orchestration, which allows a Copilot to invoke specialized sub-agents (for instance, one agent might handle scheduling meetings while another analyzes documents) . This essentially treats the Copilot as an agent-of-agents, coordinating various pieces to fulfill a user’s higher-level request. Similarly, Google has announced plans for an upgraded Google Assistant with Bard, aiming for an assistant that can, say, book a flight for you end-to-end rather than just tell you the flight options.

We also see agentic interfaces in specific domains: for example, in Adobe’s creative tools, the upcoming “Adobe Assistant” is intended to let users ask for design changes or content creation in natural language, and it will carry out sequences of operations in Photoshop or Express automatically . This is an agent-as-interface pattern – the user expresses intent, the software (agent) executes the steps. It abstracts away the menu clicks and rote tasks. Similarly, consider enterprise software: Salesforce has been working on AI agents (EinsteinGPT, etc.) that can traverse CRM data and perform actions (like logging a call, scheduling follow-ups) via simple prompts. All of these are early slices of the Agentic Web idea, confined to specific applications now but gradually networking outwards as those agents hook up to more external tools.

The web browser itself is evolving into an agentic application. Beyond Microsoft’s integration of Copilot in the Edge browser, other browsers are joining in. Opera (cited in the research as well ) introduced an AI sidebar and has experiments for an AI that can navigate pages for you. We’re heading toward a browser that might accept a query like “Find me a vintage record player under $200 and buy it” and then automatically go through search results, compare prices on shopping sites, perhaps even place the order while you watch or get a summary at the end. This is fundamentally different from our current search-and-click ritual. It’s telling that even at the OS level, companies are integrating agents (e.g., Windows AI Foundry in Windows 11 provides infrastructure for running local LLMs and agents ), which implies your future operating system might come with a built-in personal agent that understands your files, apps, and preferences – a local “web” of your own that it can act on.

2. Autonomous Task Orchestration and Multi-Agent Workflows: Another burgeoning application is what might be called “auto-complete for tasks” – systems that can take a high-level task and break it down, then execute step by step. Early enthusiasts in 2023 popularized projects like Auto-GPT and BabyAGI – essentially Python scripts that wrap an LLM and prompt it to plan and execute iteratively, even spawning new “agent” threads for sub-tasks. These open-source experiments, while often brittle, demonstrated a kind of proof-of-concept for autonomous multi-step agents. For example, Auto-GPT famously was able to receive a goal like “research and buy an affordable vacuum” and then it would autonomously search for vacuums, compare features, keep notes, and attempt to purchase (with human oversight at the final step). It revealed both the possibilities and the challenges – sometimes it got stuck or made poor decisions, but it showed an inkling of agents that proactively carry out non-trivial tasks.

Building on that, companies are now embedding more robust versions of this orchestration. Microsoft’s Azure AI Foundry Agent Service (mentioned earlier) is essentially a platform for building such orchestrations securely and scalably . They highlight the ability to chain multiple agents and tools for end-to-end business processes – think of automating a loan processing pipeline or an IT support workflow with minimal human intervention. In these cases, the agent isn’t just responding in chat; it might be interacting with databases, sending emails, fetching records, and updating systems all on its own. Microsoft also introduced the concept of an Agent ID and governance for these – because when agents start doing significant business operations, you want to track which agent did what (for auditing) and ensure they don’t proliferate uncontrollably .

A concrete illustration from the research paper is the travel itinerary planning workflow we discussed. There, one agent parsed the user request, a planner agent split the task and called four different services via MCP (weather, attractions, hotels, maps), and then a synthesizer agent compiled the results into a final itinerary . This kind of autonomous service orchestration is an early real-world use case. We can imagine similar orchestrations: for example, an “agent concierge”that plans your evening – it checks restaurant APIs for reservations, buys movie tickets, orders a rideshare, all coordinated invisibly. Or in enterprise: an “agent DevOps assistant” that monitors an app, detects an issue, files a ticket, applies a known fix or rolls back a bad deploy, and messages the team – tasks that cross multiple systems.

These multi-agent or multi-service workflows are essentially autonomous integrations. In the past, something like this would be done with manually written scripts or RPA (robotic process automation) software clicking GUI buttons. Now, with LLM-powered agents, the integration logic can be more flexible and the agent can handle unexpected results or ask for clarification if needed (via large language understanding). It’s still early – these systems need a lot of testing to be reliable – but pilot projects are underway in many companies.

3. Domain-Specific Autonomous Agents: We’re also seeing agentic ideas applied in specific verticals. For instance, in finance, there’s interest in autonomous trading or research agents (though with heavy oversight given the risks). BloombergGPT’s introduction of a finance-focused LLM hints at future financial analyst agents that could read news and execute trades under constraints. In gaming and virtual worlds, researchers created generative agents that live in simulated towns and interact; that’s not the web, but it shows how agents can have persistence and autonomous social interaction (some of those ideas could port to an online community managed by NPC-like agents). Customer service is another domain: instead of just chatbots that follow scripts, companies are trialing support agents that can troubleshoot customer issues by going through internal knowledge bases, opening tickets, scheduling repairs, etc., with minimal human input.

E-commerce platforms are exploring shopping agents – you give a prompt like “I need a gift for a 5-year-old who likes dinosaurs” and an agent browses the catalog, compares reviews, maybe even checks external sites for price matching, then gives you a recommendation or puts items in your cart. Amazon has hinted at such AI shopping assistants in development. These are direct early adopters of agentic web concepts, where the agent navigates web content (product listings, reviews) and user needs.

Another emergent application is in information retrieval and research: so-called agentic search. Instead of returning a list of links, an AI agent could itself click into those links, read them, summarize findings, and give you a synthesized answer with references. Tools like Bing’s AI chat and Brave’s Summarizer are moving that way – they already fetch content from multiple sites to answer your question, which is a step toward an agent doing the reading for you. Researchers specifically talk about moving from “user-centric retrieval to agentic information acquisition – from reactive lookup to autonomous, context-aware data gathering” . We might soon have an AI research assistant agent that given a topic, will scour databases, compile a report, even generate charts, all orchestrated in the background.

It’s worth noting that many of these early agent applications still involve a human in the loop at critical points – either to review the final action (e.g., confirm a purchase) or to provide additional input if the agent is unsure. Fully hands-off autonomy, especially in high-stakes matters, is something most developers are approaching cautiously for now (for good reason). But as confidence in these systems grows, the loop might gradually widen, and more autonomy may be granted.

Challenges and Open Questions

For all the excitement around the Agentic Web, there is a long list of challenges – technical, ethical, and practical – that must be addressed as we transition into this new era. Here we outline some of the most pressing ones, which researchers and industry experts are actively investigating:

1. Coordination & Complexity: Building and managing agentic systems is inherently more complex than traditional web apps. Orchestrating multiple agents and services without things going awry is a serious challenge. There’s potential for the “agent orchestration problem” – how do we coordinate many moving parts (agents) reliably, especially when they are adaptive and can change their behavior? Race conditions, deadlocks, or infinite loops between agents are new failure modes to consider (e.g., two agents could conceivably get stuck in a loop handing a task back and forth if not designed carefully). The proposed architectural shift to a Client–Agent–Server model means introducing a whole new middleware layer that itself must be scalable and robust. Techniques from distributed systems (like leader election, consensus, or transaction management) might need reinterpretation in an agent context. One area of research is in formalizing protocols and guarantees for agent interactions – ensuring, for example, that tasks will eventually terminate, or that resource usage doesn’t spiral if an agent goes haywire.

2. Scalability & Performance: A web full of agents raises scalability questions on multiple fronts. Firstly, running large language model agents is computationally expensive. If every user has an AI agent constantly doing stuff, that’s potentially billions of ML inferences happening, far beyond the load of today’s web (which mostly offloads computation to the client or uses lightweight API calls). This is already felt in the cost of services like ChatGPT. There is active work on optimizing model runtimes and using smaller specialized models for sub-tasks, but the fact remains: autonomy can be heavy. One particularly thorny issue is billing and cost transparency. Unlike a simple monthly subscription for an app, an agent’s activity can incur variable costs (API fees, compute time) that are hard to predict upfront. The research explicitly flags the “billing challenge” – how to create viable pricing models for agent services, so that users aren’t blindsided by a huge bill after delegating a complex task . Microsoft’s Azure Agent service likely will evolve new billing paradigms (perhaps usage-based, or outcome-based pricing). There’s also the scalability of web infrastructure: If agents are scraping or calling APIs more aggressively than humans do (because they operate faster and 24/7), could we see an agent-induced DDoS effect on sites that aren’t prepared? Web providers might need to adjust – possibly requiring authentication for agent traffic or setting up separate “agent lanes” in their services.

3. Discovery and Trust in an Agent Ecosystem: As mentioned, agents will need to discover other agents and services. But how do you ensure that an agent finds the right helper or API for a task? There’s a risk of fragmentation if multiple competing standards for discovery exist. A universal “search engine for agents” might be needed – perhaps a directory akin to DNS or package registries (npm/PyPI for services). The Agent Communication Protocol (ACP) by a Linux Foundation group hints at a future where one might query a registry for, say, “weather forecasting service” and get a list of agent-accessible options . But discovery is tightly linked to trust: how does your agent know which service to trust or which other agent is reliable? Reputation systems for agents might emerge. Microsoft’s notion of giving enterprise agents a verifiable identity (Entra Agent ID) is one approach within organizations . On the open internet, perhaps we’ll see cryptographic signing of agent identities or reputation scores. This also relates to security, because an impersonator agent or malicious service could trick other agents – e.g., a fake “weather service” could feed wrong data to sabotage a competitor’s planning agent.

4. Safety and Security: This is arguably the biggest area of concern. When autonomous agents roam the web and can take actions (including spending money, modifying data, or communicating widely), the security threat model blows up. Researchers are cataloging novel threats that come with agent autonomy. A 2025 comprehensive threat model for agentic AI notes, for example, persuasion or manipulation of agents via subtle UI or content tricks – akin to phishing humans, but here phishing the AI. An example given is using UX patterns to gradually sway an agent’s decisions (like a site nudging an agent to book premium options) . There’s also data poisoning: malicious actors could create websites full of false or misleading info tailored to mislead agents (since agents might not have the same common-sense filters humans do) . An agent could unknowingly incorporate these poisons into its plan – imagine an agent planning a route that leads a self-driving car into a trap because of bad map data. Another threat is prompt injection – if an agent reads content that includes instructions, a clever adversary could embed hidden commands that the agent then executes (this has already been demonstrated with prompt-based systems). The agent’s persistent state can be exploited too: an agent with memory could have that memory corrupted over time by feeding it subtle biases (like training data poisoning, but via interactions) .

The security issues span all layers: cognitive (tricking the agent’s reasoning), protocol-level (exploiting communication channels), and economic (tricking it into unauthorized transactions) . They can cascade – a small manipulation at the perception level could lead to a wrong decision and then a financial loss . As a result, a lot of effort is going into making agents more robust: red-teaming agents to see how they can be broken, adding guardrails to limit their actions (for example, requiring confirmation before certain high-risk actions), and monitoring their behavior with anomaly detection. One promising approach is sandboxing: running agents in controlled environments where they only have access to certain tools and can’t, say, touch your core systems unless checks are passed. Microsoft’s approach of integrating compliance controls and observability (trace logging of agent decisions) in Azure Foundry is an example – basically giving developers a way to watch what an agent is doing step by step, to debug or catch issues.

Security of the underlying protocols is also crucial. If A2A or MCP become the norm, they need to be hardened. Anthropic’s MCP spec has already gone through revisions to add OAuth and authorization mechanisms to ensure agents only access what they’re permitted . There’s talk of an MCP server registry to manage trust in available tools . We may need new security frameworks tailored to agentic operations – for instance, an agent might carry a token that limits its spending or scope (like a capability token).

Finally, there’s social and ethical security – preventing misuse. Agents could be used to generate misinformation at scale, or to automate cyberattacks (imagine spearphishing emails written and sent by an agent that also reads all the target’s social media for personalization). On the flip side, agents could also be deployed as defenders – e.g., an agent that monitors systems for intrusions or rapidly patches vulnerabilities when they’re found. It’s an arms race as usual, but now largely automated.

5. User Trust and Adoption: Even if the tech works, will people be comfortable turning over parts of their digital lives to autonomous agents? This is as much a UX challenge as a technological one. Users will need clear mental models of what their agents can do, and ways to control or correct them. There’s a risk of over-reliance (“automation complacency”) where a user stops paying attention and the agent makes a mistake. Conversely, if the agent is too conservative and keeps asking the user for permission, it loses its convenience. Designers are exploring interfaces where the agent can explain its reasoning or show a summary of planned actions before executing them, to build trust. This is a tricky balance: too much transparency and the user might as well do it themselves; too little and it feels like a black box. The research posed questions like “what new interaction primitives are required to allow meaningful human oversight without undermining the agent’s autonomy?” . Features like an “AI command log” or a “dry-run mode” where an agent tells you what it woulddo can help users gain confidence.

There’s also the aspect of ethical alignment: ensuring agents act in the user’s best interest and according to their values. If your agent is finding news for you, do you want it to filter out certain content or not? If it’s acting on your behalf in a marketplace, should it negotiate hard or be polite? People might need to train their personal agents on their preferences – effectively a new kind of personalization (not just content preference, but decision-making style).

6. Interoperability and Standards Battles: We have to acknowledge the potential for fragmentation. Right now, multiple big players are pushing their visions – OpenAI has its ecosystem (plugins, etc.), Microsoft and Anthropic are advocating open protocols, Google has its A2A, and there will be others. For the Agentic Web to really flourish like the original Web, a strong set of open standards (analogous to HTTP, HTML, etc.) is needed so that an agent can operate across the whole internet, not just walled gardens. The early signs are promising, with major companies surprisingly collaborating (e.g., Microsoft joining the MCP steering committee alongside Anthropic , and Google’s A2A being open). But we might see competing approaches – perhaps one company’s agents only fully work with their cloud or their app store. The community (developers, perhaps guided by groups like W3C or a new body) will need to push for interoperability so that we don’t end up in a scenario where your “personal AI” is as siloed as, say, iOS Siri vs Android Assistant, unable to access certain services because of proprietary locks. The history of the Web suggests that openness tends to win in the long run, but the history of mobile suggests platforms will try to maintain control. The Agentic Web might replay some of those dynamics.

The Agentic Web vs. Web3 and the Decentralized Web: Converging or Competing?

It’s worth comparing the Agentic Web vision to other “future of the internet” movements – chiefly Web3 (decentralized web), but also the broader decentralized web efforts like Solid or IPFS. At first glance, they seem to be different beasts: Web3 is about who owns/runs the web (pushing for decentralization of data and blockchain-based trust), whereas the Agentic Web is about how the web is used (autonomously, by AI agents). However, there are interesting overlaps and potential synergies, as well as divergences.

Overlaps/Complementarity: Both visions are reactions to the current centralized, platform-dominated web and aim to empower end-users. Web3 proponents want to give users control over their data, identity, and finances through decentralization and cryptography. Agentic Web proponents want to give users control over their time and tasks by offloading work to intelligent agents. In an ideal scenario, these two visions could complement each other: imagine an open agentic web operating on decentralized infrastructure. For example, your agent could use a decentralized identity (DID) to authenticate on your behalf, use cryptocurrency wallets to pay for services, and store data on distributed networks like IPFS or Filecoin, all without a central company’s permission. The research paper notes that “the integration of blockchain presents promising opportunities” for the Agentic Web’s economic foundation, enabling trustless interactions and autonomous transactions between agents . Projects like ChainOpera that merge Web3 and agentic AI show it’s feasible for an agent to interact with smart contracts on a blockchain, potentially creating self-executing agreements between agents . So one can imagine a future where an agent, say, finds a service and instead of a traditional API key, it uses a blockchain-based token to access it and pays via crypto per call – all transparently logged on-chain. That could solve some trust issues (since transactions are public and verifiable) and make agent interactions more open.

Another overlap is the emphasis on open protocols. Web3 is built on open standards (Ethereum’s protocols, etc.), and as we’ve seen the Agentic Web is also pushing open protocols like A2A and MCP. Both rely on the idea of interoperability as a key – in Web3 it’s across blockchain apps (composability), in Agentic Web it’s across agents/services. There’s even an interesting prospect where agents themselves could run in a decentralized way – for example, multiple nodes collectively running an agent AI to avoid dependency on a single cloud provider (though the technical complexity of distributing an LLM is non-trivial). Some early efforts like blockchain-based AI marketplaces (SingularityNET, etc.) tried to marry AI services with decentralized hosting, but they were limited by AI tech at the time. With LLMs and more consensus on standards, maybe we’ll see decentralized networks of agents that anyone can contribute to or benefit from.

Divergences/Conflicts: On the other hand, Web3 and Agentic Web have different primary focuses, and in some respects could be seen as competing priorities. Web3’s ethos is decentralization and trustlessness – removing intermediaries. The Agentic Web, as currently envisioned, doesn’t inherently require decentralization; an agent could be entirely run by a big company on proprietary models, essentially adding more intermediaries (your agent might become the new intermediary between you and various services). It’s possible that without deliberate effort, the Agentic Web could actually strengthen the power of whoever provides the best agents – for example, if everyone relies on an AI from Company X to handle their tasks, that company becomes extremely influential (like an ultimate “platform”). In contrast, Web3 wanted to break platform monopolies using tech like blockchain. There’s a bit of a philosophical difference: Web3 often emphasizes user sovereignty (own your data, your keys), whereas the Agentic Web emphasizes user convenience via autonomy. Ideally, we want both, but one can imagine tensions – an agent might optimize for convenience by using centralized services that are easier or cheaper, bypassing a more decentralized but slower option.

Another area of divergence is economic models. Web3 introduced things like tokens, crypto incentives, and decentralized finance. The Agentic Web economic dimension, as discussed, envisions agents possibly using those but also possibly just interacting with traditional currency via APIs. Not all Agentic Web scenarios need blockchain; many could run on fiat and centralized databases. For instance, your agent can use your credit card on a website just fine – no blockchain needed. So one could see an agent-driven future that still runs on mostly Web2 infrastructure, thus not achieving the decentralization that Web3 advocates argue is crucial for privacy and freedom. Conversely, Web3’s decentralized apps (dApps) currently often have usability issues; adding agents could actually help them. For example, managing crypto wallets and DeFi is complex for humans – an agent could do it for you, making Web3 more accessible.

There’s also a difference in community and development style: Web3 has been community-driven, open-source heavy, sometimes regulatory-challenged; the Agentic Web so far is being pushed by both academia and major industry players like Microsoft, Google, Anthropic. It has more corporate weight behind it at the moment (which is good for resources and standardization, but might lead to more corporate control if not checked). Web3 people might be wary that Agentic Web could just mean “AI intermediaries run by big tech controlling more of our digital life.” On the flip side, Agentic Web folks might view some Web3 tech as useful tools but not the end goal; they might adopt the parts that make sense (like blockchain for transactions or identity) without embracing the full decentralize-everything philosophy.

The likely outcome is that these visions will partially converge: we’ll see hybrids like autonomous agents in decentralized finance (already being studied, as cited by Ante 2024 on autonomous AI in DeFi ) and personal agents that use decentralized identity and storage. We’ll also see areas where they diverge: for example, a corporate enterprise agent network might have zero blockchain or decentralization – it might all be behind one company’s walls, but still be “agentic” in function. Meanwhile, decentralized web proponents might build agentic layers that avoid centralized AI models (maybe using open-source models running locally or on decentralized compute).

In terms of overlap in goals, both want a web that is more user-centric and less about giant middleman platforms dictating terms. They approach it differently: Web3 says “make platforms obsolete via distributed trust,” Agentic Web says “make interfaces obsolete via automation.” Perhaps the ultimate future merges both: platforms become protocols (as Web3 wants) and interactions become automated (as Agentic Web wants). That would indeed be a very different web – open, intelligent, and automated. But there’s a long way to go, and it will require bridging communities and ensuring the solutions from one side inform the other. For instance, the Agentic Web will need solutions to trust and verification – Web3 has some answers for that (like immutable logs, smart contracts), so collaboration could be beneficial.

Conclusion: Weaving the Next Web

The idea of the Agentic Web represents a bold reimagining of what the internet is for and how it operates. Instead of a global network primarily connecting people to information, it becomes one connecting intelligent agents to tasks and services – with humans guiding at a high level but increasingly offloading the minutiae to our digital proxies. It is, in a sense, the internet finally evolving from a read/write medium of documents and clicks into an action-oriented environment where things get done on our behalf in the background . This marks a shift as significant as the move from static HTML pages to the dynamic app-driven web.

The potential benefits are immense: imagine reclaiming hours of your day as your agents handle routine emails, form-filling, shopping comparisons, or scheduling. Imagine businesses that run 24/7 with AI agents coordinating supply chains, customer support, and analytics in a seamless flow. The web could become more productive and personalized, with less waiting on human response times and fewer manual hurdles. It could also become more inclusive – someone who isn’t tech-savvy might still accomplish complex online tasks by simply telling an agent what they need in plain language.

However, this future comes with significant caveats. To truly trust agents with our goals – from the trivial to the life-critical – we need to ensure they are robust, secure, and aligned with our interests. The challenges of coordination, safety, and ethical design we discussed are not trivial; they will require careful engineering, interdisciplinary research, and likely new regulatory frameworks. There are also broader societal questions: How will this affect jobs that involve online work (travel agents, personal assistants, even software developers)? Do we risk creating a class of “AI middlemen” that control access to information and services? How do we ensure users remain in ultimate control and can override or disconnect agents that go astray? These questions have echoes of past tech disruptions but also novel twists given agents’ autonomy.

One possible outcome is that an open Agentic Web thrives, much like the early web did – with common standards ensuring a level playing field where anyone’s agent can talk to anyone’s service. In that scenario, we might see a flourishing of innovation, with countless specialized agents and services forming a rich ecosystem. Another outcome to guard against is a more siloed agent world, where a handful of AI platforms mediate most agent interactions (imagine if, say, your personal AI is only effective within one company’s ecosystem). The fact that companies are open-sourcing protocols and collaborating is a positive sign , but continued advocacy for openness will be needed.

In the mid-1990s, the original web’s inventors spoke of “weaving the Web,” envisaging a connected world of information (Tim Berners-Lee famously titled his book Weaving the Web). Today, in 2025, we are beginning to weave a new web – one made not just of hyperlinked documents, but of interlinked intelligent agents. The threads of this web are protocols like MCP/A2A, AI models, and collaborative frameworks; the fabric is still being knit together through research and experimentation. As one survey paper put it, this is a leap from AI that simply responds to prompts to “agentic AI” characterized by proactive, independent decision-making and execution . It’s a leap that will require not just technical ingenuity, but also foresight to ensure it serves humanity.

The next few years will likely bring rapid progress. We’ll see more agent-enabled features in the apps and devices we use. We’ll also likely see high-profile missteps – an agent that does something embarrassing or harmful – which will be important learning experiences to refine the limits and safeguards. Through it all, developers and users will need to stay informed and engaged. Just as the shift to mobile computing required rethinking design and security (e.g., responsive design, app permissions), the shift to agentic computing will require new mental models and best practices.

For developers, this could mean learning new frameworks for prompt engineering, agent orchestration, and protocol integration. For users, it means learning how to effectively delegate to an AI (what instructions yield the best results, how to review an AI’s output). For policymakers, it means updating laws around digital agents (assigning liability, ensuring transparency, maybe treating some AI decisions similar to human decisions in legal contexts).

Standing at this inflection point, one can’t help but feel both excited and a bit apprehensive. The Agentic Web has the aura of science fiction becoming reality – the long-envisioned “digital butler” or “autopilot for life” concept materializing. It holds the promise of freeing us from digital drudgery and expanding what we can accomplish. But as with any powerful tool, how we wield it will determine the outcome. If we weave this new web wisely – with open standards, security, and humanity’s interests at the forefront – it could truly become the next great phase of the internet, an era where agents augment human capability at a global scale. The loom is in motion; the pattern we knit now will shape the fabric of our digital lives for decades to come.

Sources:

Yang et al., “Agentic Web: Weaving the Next Web with AI Agents,” arXiv preprint (July 2025).
Microsoft Build 2025 announcements on the open agentic web and Azure AI agent services .
Anthropic’s introduction of the Model Context Protocol (MCP) and Google’s Agent-to-Agent (A2A) spec, as summarized in Can Demir, “MCP × A2A × RAG — The Trio Conducting the Agentic Web” (Medium, Jul 2025) .
Safety and security analysis from Narajala & Narayan, “Securing Agentic AI,” and Yang et al on novel threats .
Adobe Blog, “Our vision for accelerating creativity and productivity with agentic AI” (April 2025), for industry view on agentic AI in creative tools.