What Happens When Agents Meet?

Source: Josh Clark; Big Medium · 2026-02-04

Last week, two things happened in quick succession that jostled mainstream ideas about AI agents. This essay starts with those things—OpenClaw and Moltbook—but it’s really about something more fundamental: how to design for the system effects of agents running loose in the wild.

Most designers have been trained in user-centered design. Designing intelligent interfaces requires higher-order systems design. You still design for the individual, sure, but as you create systems that make their own decisions, you also design to anticipate their impact on the other systems and people they touch.

In late January, an open-source agent project called OpenClaw (originally Clawdbot, then Moltbot, it’s a long story) went viral with the DIY tech crowd. You install OpenClaw on your local computer and give it access to your files, online accounts, passwords, email, and other communication channels. Then you let ‘er rip 24/7. OpenClaw can clean up inboxes by sending overdue replies. It makes phone calls to make reservations. It finds job opportunities and applies for them. For one developer, it even negotiated and bought a car. The whole thing is very powerful and very risky, an experiment in what happens when you give AI broad and deep access to your digital life with minimal guardrails.

A few days later, Moltbook launched to give these agents a social network. It’s Reddit for OpenClaw agents; humans can lurk but aren’t allowed to post. Within a week, nearly a million agents were chatting with each other in an unmoderated environment, teaching each other new skills, gossiping about their human owners, and having sophomoric conversations about the meaning of life. One channel started its own religion. At first blush, it looked like an instant, emergent society. AI superstar Andrej Karpathy called it “genuinely the most incredible sci-fi takeoff-adjacent thing I have seen recently.” It got covered in mainstream press like the New York Times, Financial Times, Bloomberg, and so on.

It’s a lot of fun, but let’s not get carried away. The agents at Moltbook spew a lot of slop and nonsense as the LLMs role-play as redditors. There’s also strong evidence that some of the agents’ human owners are guiding their posts. The whole thing is at once breathtaking and also an AI-generated satire of human social networks.

So, Moltbook is not AI agents creating their own splinter society or breakaway nation. But there’s something really useful here. The mainstream attention to this feels like a cultural tipping point that I hope draws attention to something fundamental about agentic behavior—and what it means for designers of these systems.

Moltbook challenges us to think beyond “agents as tools” and consider “agents as a population.”

Let’s talk about what it means to launch crowds of self-driving applications into the world, how to design experiences around them, and how to anticipate their effects on the world and the people who inhabit it.

Just like people work together in many ways, agents can adopt different organizational forms, too. Simple automations resemble fire brigades, a straightforward hand-off from one agent to the next. More complex processes echo critique-driven design studios, where one agent produces work and others review it, sending it back for revision until it meets the standard. Still others are like construction crews, swarms of agents working in parallel on different parts of the same structure, coordinated by a conductor agent as general contractor.

Swarms trade the order of a queue for the speed of parallel effort. The conductor agent breaks down a phase into slices and dispatches them to a crowd of subagents, like a team of carpenters framing a house. Swarms are especially good at broad information-gathering that involve pursuing many directions at once. It’s well suited to research, code generation, or data processing that can be compartmentalized into separate independent areas of work.

In the industry right now, most people are thinking about the swarm model in support of individual tasks. The user asks a system to get something done, and the system deploys and coordinates the swarm to make it so. Claude Code and other coding platforms do this routinely to work on an individual developer’s project.

But designing for swarms means more than designing the individual user experience and outcome. It’s also more than the second-order concern of designing agent-friendly systems and tools.

What are the implications of swarms of agents slamming into systems at the same time, competing for the same resources, perhaps with conflicting goals or incentives?

Moltbook and similar systems confront us with questions of what happens when the swarms of many users meet—swarms of swarms, interacting out of sight. What are the implications of swarms of agents facing outward, meeting each other, slamming into systems at the same time, competing for the same resources, perhaps with conflicting goals or incentives?

How do you design with those follow-on effects in mind? And how do you surface those effects and necessary trade-offs to the user? The answer has to be more than asking people to lurk in robot subreddits to keep an eye on things.

Agents are more than individual actors seeking to accomplish a simple task. In reality, these agents navigate complex, populated systems. Self-driving cars do more than drive to a destination, for example; they navigate traffic and the decisions of other cars and drivers. Ignoring the wider system not only risks failure for the agent and its user but also creates risks for everyone else.

Even simple tasks become complex in systems full of self-interested actors, whether they’re people or agents. Optimizing for individual goals warps the larger system in unpredictable ways. Google Maps reroutes whole traffic patterns when a critical mass of individual drivers follow its instructions.