The Internet Was Built for Humans. Meta Muse Is Showing Us What Happens When the Users Become Agents.

For most of the Internet’s history, software has been designed around a simple assumption: somewhere on the other side of the screen is a human being. That human searches, reads, clicks, fills out forms, compares choices and eventually completes some action. We have spent decades refining interfaces around that model, from search engines and ecommerce sites to banking systems and business applications.

Meta’s Muse personal AI agent suggests that assumption may be starting to change.

Muse is not simply another chatbot competing to provide better answers. Meta is positioning it as a personal agent that can perform work on behalf of a user, operating through a browser and connected services to carry out tasks rather than merely describe how those tasks could be performed.

That distinction matters because it changes the basic interaction model of the Internet.

For decades, the dominant model has been human-to-application. Agentic systems introduce an intermediary that can interpret intent, choose where to go, determine what actions to take and interact with multiple systems on the user’s behalf. The human may still be the ultimate customer, but the software agent increasingly becomes the entity actually using the Internet.

Who Is the User?

This sounds philosophical, but it quickly becomes a practical software architecture problem.

Imagine telling an AI agent to find an artist who paints emotional landscapes, commission a work based on a photograph, keep the price under $1,200 and make sure it can be completed before an anniversary. The agent could potentially search artists, compare portfolios, examine commission terms, ask about availability, submit reference material, exchange messages and eventually arrange payment.

The person commissioning the painting might never visit the artist’s website.

From the artist’s system, who was the user? The human who initiated the request? The AI agent that actually communicated with the application? The company operating the agent? In practice, all three may be involved in different ways.

Most software systems are not designed to represent that relationship very well. Traditional applications tend to think in terms of a single authenticated user account. Agentic systems require something more expressive: a principal, an agent, a role and a defined scope of authority.

The important abstraction is no longer merely identity. It is identity plus delegated authority.

Authentication Is No Longer Enough

The software industry has spent years improving authentication and authorization. Authentication asks who you are. Authorization asks what you are allowed to access.

Agents add another question: what decisions is this software allowed to make on your behalf?

An agent might be allowed to read email but not delete it. It might be permitted to make a restaurant reservation but require approval before canceling one. It might be allowed to buy routine supplies under a certain amount while requiring confirmation for larger purchases. Those are not simply access-control decisions. They are delegated-authority decisions.

This creates a need for systems that can answer questions such as who authorized an action, which agent performed it, what authority had been delegated, whether human approval was required and whether the action can be reversed. These questions look very similar to the questions organizations already ask about employees, contractors and service accounts.

That similarity is important. As agents become more capable, software systems may need to treat them less like passive tools and more like actors operating within organizational boundaries.

The Browser May Become Infrastructure

For decades, companies have fought over browsers, search engines, app stores and home screens because those products controlled access to the Internet. AI agents could move that control point again.

If I ask an agent to find me a hotel near Union Square for Friday night, under a fixed budget, with parking and consistently good reviews, I may not care whether it checks Google, Expedia, Booking.com, Marriott or the hotel’s own website. I want the result.

The browser may still exist underneath, but I may never see it.

That changes the economic importance of the user interface. If the agent chooses where to go, then control over discovery may shift away from websites and search pages toward the agent deciding which sources are worth consulting.

The browser becomes infrastructure. The agent becomes the interface.

Amazon and Shopify Reveal the Conflict

The differing reactions of ecommerce companies to agentic shopping illustrate why this matters.

A company like Amazon derives substantial value from controlling the full customer journey: search, sponsored results, recommendations, product pages, related products and checkout. An independent AI agent can potentially bypass much of that experience. If a user simply asks an agent to find the best 12-inch compound miter saw under $600 from a reputable seller, the agent does not necessarily need to expose the customer to dozens of search results, sponsored placements or merchandising recommendations.

A commerce platform that primarily benefits when merchants complete transactions has a different incentive. If an agent helps customers find and buy products more efficiently, that platform may view agents as another distribution channel rather than a threat to its interface.

The conflict can be represented fairly simply.

The more value a company derives from controlling the interface itself, the more disruptive a third-party agent can become.

What Happens to Advertising?

Advertising may be one of the industries most directly affected by this shift.

Today, businesses spend enormous amounts of money persuading humans to look at things. Search ads, display ads, sponsored products, affiliate recommendations, retargeting and promoted listings all depend on capturing human attention.

An AI agent does not necessarily need to pay attention in the same way.

Suppose I ask an agent to find a reliable color laser printer capable of handling 5,000 pages per month, with inexpensive toner and a price below $700. The agent could compare specifications, ownership costs, reviews and current prices without ever presenting me with a conventional search-results page.

From the user’s perspective, that may be an improvement. From the advertiser’s perspective, it creates a difficult problem: how do you advertise to software that is trying to optimize for the user’s stated objective?

This may eventually shift part of digital marketing away from presentation and toward machine-readable credibility. Businesses could find themselves optimizing not only for search engines, but for agents that need structured pricing, availability, policies, specifications, reputation signals and transaction capabilities.

The central question may become less “How do we persuade a visitor?” and more “Can an agent understand exactly what we offer and confidently transact with us?”

Websites May Need Two Interfaces

One likely consequence is that businesses will increasingly maintain two interfaces.

The first is the traditional human-facing interface: visual design, branding, photography, navigation, marketing copy and interactive controls. The second is an agent-facing interface built around capabilities, structured data, identity, authorization, pricing, availability and transaction semantics.

Consider a process-service platform such as Blackbird Serve. Today, a lawyer might visit the website, find the registered agent, enter service information, upload documents and submit the job. An AI agent might not need the website at all. It might only need functions equivalent to:

findRegisteredAgent()

quoteServe()

submitService()

getServeStatus()

retrieveProofOfService()

This changes the architectural question. Instead of asking only what screens a user should see, we begin asking what roles can interact with the system, what capabilities they can invoke and what authority they possess.

That architecture works whether the role is performed by a person, another application, an AI agent or eventually a physical machine.

Security Becomes More Serious When AI Can Act

Agentic systems also turn existing AI weaknesses into operational security problems.

Prompt injection is a good example. If a conversational model reads malicious instructions hidden on a webpage and produces a poor answer, the result may be inconvenient. If an autonomous agent has access to email, calendars, files, browser sessions, credentials or purchasing authority, malicious instructions can potentially trigger real actions.

The risk model changes because the AI is no longer merely interpreting information. It is exercising authority.

That means agent security requires more than protecting credentials. It requires reasoning about which inputs are trusted, which actions are sensitive, when confirmation is required, how actions are audited and how much damage an agent can cause if manipulated.

This is one of the clearest differences between conversational AI and agentic AI: a chatbot can make a bad suggestion; an agent can perform a bad action.

Privacy Becomes a Question of Context

A useful personal agent becomes valuable largely because it knows context.

It may understand your email, schedule, projects, purchasing habits, preferences, relationships and financial constraints. Future systems could also incorporate information from cameras, microphones, wearable devices or physical environments.

That creates tremendous utility, but it also creates an extraordinary concentration of personal context.

The privacy question is no longer limited to whether a company stores a conversation. It becomes a question of how much of a person’s life an agent must understand in order to be effective, how long that information is retained, which connected systems can access it and what happens when one delegated task requires information gathered from another part of the user’s life.

The better the agent understands the user, the more useful it becomes. The same characteristic also increases the consequences of misuse, unauthorized access or poorly designed permissions.

Delegation Can Become Surprisingly Complicated

Agentic systems can also create unusual chains of representation.

A user may delegate work to an AI agent. That agent may call another service, launch a specialized subagent or potentially involve a human operator. The resulting chain could look something like this:

At that point, several questions become important. Who exactly is representing the user? What information does each participant receive? Which participant is authorized to make commitments? Is the outside business aware that it is dealing with an intermediary? Is the user aware that another human or service has entered the chain?

These are not merely AI questions. They are questions about identity, delegation, consent and responsibility.

Organizations May Need to Be Modeled Differently

The same architectural shift could eventually affect how organizations themselves are represented in software.

Today, organizational charts usually assume that roles are filled by people. In an agent-driven environment, a manager may oversee a mixture of humans and software agents.

That makes the distinction between “person” and “computer” less useful at the architecture level. The more durable abstraction is role, responsibility, authority and capability.

A role may currently be performed by a person. Later it may be performed by software. Eventually it might be performed by a robot or some combination of all three.

Good architecture should not have to be redesigned simply because the processor performing a role has changed.

This Is Not Really an Article About Meta Muse

Muse is useful because it makes these issues visible in a product people can understand, but Muse itself is not the larger story.

The larger story is a change in abstraction.

The Internet was originally populated by humans operating computers. Later, software began communicating directly with other software through APIs. Now autonomous software is beginning to operate systems originally designed for people.

That creates an Internet populated by both humans and agents.

The systems we build will increasingly need to recognize both. Businesses will have to decide how agents are allowed to interact with them. Identity systems will have to represent delegation. Authorization systems will have to express limits of authority. Commerce systems will need to determine when agents can transact. Security systems will need to assume that untrusted content may attempt to manipulate autonomous software.

Designers may also discover that some of their most important users never see their user interface.

For decades, the dominant software-design question has been:

How do we make software easier for people to use?

The next question may be:

How do we build software when the entity using it may not be a person?

Meta Muse gives us an early look at that world. Whether Muse itself ultimately dominates the market may be much less important than the architectural shift it represents.

The Internet was built for humans.

It may soon have many other users.