When AI no longer just answers questions but begins to own its own “computer,” artificial intelligence is entering a new phase.

Over the past two years, the most obvious application form of large language models (LLMs) has been chatbots. Users ask questions and the AI generates answers; when asked to write code, draft reports, or analyze data, the AI helps by producing text or code.

By 2026, however, the AI industry is undergoing an important shift: AI is moving from “answering questions” to “doing things on behalf of people.”

Prominent examples of this change include Meta’s Muse, Manus, WorkBuddy, and the open-source Agent project OpenClaw. At the same time, TypeSafe’s Jev approaches the Agent architecture from a different angle: it reduces reliance on large language models for every decision by delegating many simple, fast judgments to a dedicated “System One” decision model.

Viewed together, these developments reveal a clearer technical evolution:

AI is transitioning from a “talking brain” into a digital agent that can have a computer, call tools, remember users, execute tasks, and run continuously.

1. What exactly is an AI Agent?

To understand an Agent, start by looking at how traditional chatbots work.

Past AI was basically:

User asks → LLM thinks → Outputs an answer.

For example, a user might say:

“Help me find a flight to Tokyo next week.”

A traditional AI might tell you which sites to visit, how to search, or give suggestions.

An Agent’s goal goes further:

Understand the objective → Search web pages → Compare flights → Check times → Fill in details → Ask the user when needed → Complete the task.

In other words, AI no longer merely provides information; it begins to gain “action capability.”

Therefore, a true Agent typically requires several basic components:

a model, memory, tools, a compute environment, and execution permissions.

The model handles reasoning, memory preserves context, tools connect to the internet and applications, and the compute environment acts like the AI’s own “computer.”

This is one of the most important changes in Agent technology today.

2. Meta Muse: AI begins to act like a “personal butler”

Meta’s recent Muse can be seen as a consumer-facing personal AI Agent.

Meta designed a dedicated Muse Secure VM, an isolated secure virtual compute environment. Muse can use a browser, access the internet, process tasks there, and limit high-risk operations through permission controls.

That means Muse does not simply reply in a chat window:

“You can go to this website to book a ticket.”

Instead it moves toward:

“I’ll take care of this for you.”

For example, a user could tell Muse:

“Help me plan a trip.”

In principle, the Agent can search flights and hotels, compare options, and request user confirmation for sensitive operations like payments.

Meta also aims to connect Muse gradually to WhatsApp, Instagram, Facebook and AI glasses.

This implies an important change:

An Agent may no longer be a standalone app that users must open; it could become an underlying layer in users’ daily digital lives.

If this direction fully matures, users might not even think about “using an AI.”

They would simply say:

“Handle this for me.”

3. Manus: more like an “AI employee”

If Muse is closer to an “AI butler,” Manus is more like an “AI employee.”

Manus’s core idea is:

Give the AI a complete task, not step-by-step instructions.

For example:

“Research the U.S. AI chip market and produce a report.