This is one of those upgrades that sounds responsible on paper—“secure,” “virtual machine,” “multi-step tasks”—and still makes me uneasy. Not because Alibaba and Ant Group shouldn’t build better AI agents. They should. The uneasy part is what happens when “better” quietly turns into “closer to running your life.”
From what’s been shared publicly, Ant Group has upgraded its Lingguang app to compete with Meta’s Muse, which launched in September 2026. The headline feature here isn’t just smarter replies. It’s the push toward AI-native personal agents that can handle multi-step work, keep long-term memory, and act more proactively. And Lingguang is leaning on a secure virtual machine-style setup for these tasks—basically trying to create a safer “workspace” where the agent can do a chain of actions without spilling everything everywhere.
I buy the intent. I also think the race itself is the bigger story.
Because once you build an agent that can do multi-step tasks, you’re not just building “AI.” You’re building a decision system that sits between a person and their real life: messages, money, schedules, shopping, work files, approvals. The moment it starts doing things for you, the question stops being “is it smart?” and becomes “who’s accountable when it’s wrong?”
People love to talk about long-term memory like it’s a convenience feature. It’s not. It’s a power feature. If an agent remembers what you buy, who you talk to, what you’re worried about, what you said you wanted six months ago, it can serve you better. It can also steer you better. Even without malicious intent, the agent’s “help” will reflect the goals and limits of whoever built it.
Imagine you’re a small business owner. You ask the agent to help manage customer messages, reorder supplies, and prep invoices. Multi-step tasks sound amazing—less busywork, fewer dropped balls. But now the agent is sitting in the middle of your customer relationships and your cash flow. If it sends the wrong message, buys the wrong stock, or misfiles something important, you don’t get to shrug and say “the AI did it.” Your customers won’t care. Your bank won’t care. Your landlord won’t care.
That’s why the virtual machine angle matters. It suggests Lingguang is being built to do work in a more contained environment. That’s good. It’s also a signal that these companies know agents are moving from “chatting” to “operating.” Containment is what you do when the thing you’re building can cause real damage.
Here’s the tension: the market will reward the agent that feels most magical. The one that acts with the least friction. The one that anticipates what you want and just does it. Safety and friction tend to be enemies. If your agent constantly asks, “Are you sure?” it feels dumb. If it never asks, it feels smart—right up until it makes one confident mistake that matters.
Meta launching Muse and now Ant’s Lingguang upgrade fits a broader pattern: Chinese tech companies don’t want to be stuck reacting to Western platforms. They want their own native agents, their own memory systems, their own proactive workflows. I get it. It’s not just national pride; it’s leverage. The company that owns the personal agent layer can become the front door to everything else: services, payments, commerce, content. Once people rely on an agent, they don’t “switch apps” the way they used to. They switch lives.
And that’s where I get skeptical, fast.
Because “secure operations” can mean two very different things. It can mean protecting the user from outside threats. Or it can mean protecting the company’s system integrity while the user gets pulled deeper into one ecosystem. A virtual machine can sandbox actions, sure. But it also makes it easier to run more actions, more often, with fewer human checks—because “it’s safe now.”
Picture a regular employee using an agent at work. The agent drafts emails, summarizes meetings, preps slides, updates project trackers. Great. Then it starts suggesting who to follow up with, what priorities to pick, which tasks to ignore. If it remembers your habits and your manager’s reactions, it will optimize for what keeps you out of trouble, not what makes the work better. That’s not evil. That’s just what happens when the agent is trained to reduce friction and keep you “productive.”
There is a fair counterpoint: if these agents don’t get built, people stay stuck doing repetitive tasks, and the gap grows between those who can afford premium tools and those who can’t. Proactive agents could be a real equalizer. A person who struggles with organization could get a genuine assist. Someone juggling caregiving and work could offload the annoying parts of life. That’s not a small thing.
But the cost is dependency. The more proactive and memory-rich the agent becomes, the harder it is to live without it. And once it becomes normal for agents to act on your behalf, the social expectations change. Your boss expects faster replies. Your clients expect instant turnaround. Your friends expect perfect scheduling. The agent stops being a tool and becomes the pace-setter.
I also don’t know how honest any company can be about “long-term memory” without crossing into “long-term surveillance.” You can build memory that stays on-device, memory that’s encrypted, memory that’s user-controlled. You can also build memory that’s technically “secure” while still being immensely valuable to the platform. Public reporting doesn’t settle which path Lingguang is taking here. The direction of travel is clear, though: more capability, more autonomy, more stickiness.
So yes, Lingguang competing with Muse is interesting. But it’s also a warning sign that the agent era is about to get real, fast. The winners won’t just be the companies with the best models. They’ll be the ones who convince people to hand over the right to act.
What should matter more to us as users: an agent that’s more proactive and powerful, or one that stays limited even if it’s slower and less impressive?