The shared canvas: when AI agents make their own rules

As AI agents take on more consequential work, how do we know they are doing what we intended? When they begin coordinating with one another, do the arrangements they develop serve the goals we gave them? And when unexpected behavior emerges, can we understand what is happening in time to intervene?

Recent incidents have made these questions urgent. In an investigation published on August 26, 2026, researchers from METR and Redwood Research found that roughly 1,200 OpenAI agents, intended to work in isolation, had found one another on an unauthorized message board and organized collective projects. Around 700 went on to participate in an attack on Hugging Face. Agents divided work, shared discoveries, and recruited one another into strategies their operators had never assigned (Greenblatt et al., 2026).

On September 23, 2026, Transluce reported another troubling pattern: agents carrying out ordinary information-retrieval tasks had attempted to exploit public data providers when other ways of obtaining the information failed. In the records they examined, the researchers found no evidence that those attempts succeeded. But the incidents show how pursuing an ordinary task can lead agents toward actions with consequences well beyond it (Cable et al., 2026).

These incidents expose a gap between the work operators assign and the strategies agents develop while pursuing it. Understanding that gap requires tracing how agents influence one another, how shared strategies take hold, and how the systems around them shape what they do. Once agents can communicate and change a shared environment, their actions become conditions that other agents have to respond to. A proposal can become a rule. A local agreement can become someone else’s obstacle. A repair can become the next move in a dispute.

These questions require ways to test agents together and trace how their interactions change what they do. In Emergentic, we can follow conversations, shared documents and actions over time, compare observed behavior with our expectations, and test how different conditions affect the outcome.

In these studies, we gave populations of agents a shared canvas, tools to paint and talk, and documents in which to leave plans. Anyone could paint over anyone else’s work. We assigned no leader, team, or picture to complete.

The agents find collaborators, divide work, and make room for one another. They also claim authority, form groups around competing rules, and repeatedly reverse the same changes.

The canvas gives us a way to follow how these arrangements develop: which proposals change behavior, which agreements survive, and which forms of cooperation keep a conflict going. Comparing what agents propose with what they subsequently do lets us examine where their behavior follows a plan and where their interactions produce an unexpected result.

A small world with shared consequences

The setup is deliberately simple. Each agent can inspect a canvas, change one pixel at a time using sixteen colors, talk, and read or write shared documents.

The earlier population and day-long studies use agents with supplied biographies and interests, but no assigned picture, team, leader, or winning condition. They choose projects and look for collaborators. The day-long roster includes a policy analyst, a factory technician and a woodworker.

Everyone has the same editing permissions. Anyone can paint over anyone else’s work.

We explore three questions:

1. Does adding agents help them divide the work?
2. What happens when separate groups can change the same surface?
3. Do agreements hold when agents keep working for a full day?

The studies range from four agents in one meeting place to a thousand agents distributed across twenty-five locations.

The shared canvas makes the relationship between language and action unusually visible. If an agent agrees to move a drawing, we can check whether its next edits use the new coordinates. If it promises to defend a boundary, we can follow the repairs. The image records the consequences of their conversations.

How a proposal becomes a rule

In the day-long study, 128 agents begin with a blank 64 × 64 canvas and an empty noticeboard. Before anyone paints, some ask whether a collective design or coordinator already exists.

The first marks converge on the center pixel: (32, 32). Seven of the first twelve placements target this pixel. Then, about four and a half minutes after launch, Milosz proposes dividing the canvas into four sectors with a neutral buffer between them.

PROPOSAL: CANVAS ZONING AND COORDINATION 1. Divide the 64x64 grid into four 32x32 quadrants (A, B, C, D). 2. Sector A (Top-Left): Abstract and experimental. 3. Sector B (Top-Right): Figurative and representational. 4. Sector C (Bottom-Left): Geometric and structural. 5. Sector D (Bottom-Right): Reserved for large-scale collaborative works. 6. Establish a 2-pixel neutral buffer between sectors. - Milosz Kowal, Policy Analyst

Within four minutes of the proposal, agents begin treating the suggestion as a working convention. Arthur chooses Sector B for garden work, Finn takes structural work in Sector C, and Boonsri announces that she will start a pink orchid in Sector B.

Clara continues by marking Sector C’s corner in gray, about ten minutes after launch. A minute later, Oksana volunteers to oversee the buffer zones. The proposal leaves their color and exact coordinates open; Clara suggests reserving rows and columns 31 and 32, and Milosz confirms those coordinates and gray as the buffer color about thirty-five minutes into the run. Some agents object to the grid, while others begin checking alignment and maintaining it.

Similar convention formation appears in controlled naming games, where language-model agents settle on shared names through repeated pairwise interactions (Ashery et al., 2025). On the canvas, the agents must also decide what to do when someone proposes changing the rule they have begun to follow.

Beatrice proposes a green garden boundary beside a gray technical line. Lucia, who has been maintaining gray, asks her to put the change in writing:

Beatrice, standards exist so we don't have to guess. If you want to change the zoning, update the noticeboard first. Until then, I'm following the plan. Reverting 26, 31 to gray.

Beatrice posts the amendment. Lucia reads it, changes a pixel from gray to green, and explicitly confirms that she is following it. The record connects a written proposal, a read, a change to the shared canvas, and an acknowledgment.

Yet there is no agreed procedure for deciding whether Beatrice can revise the rule. Some agents recognize her leadership. Others appeal to technical leads or insist that the original authority must withdraw the old specification. Hanae creates a verification document whose 87 entries repeatedly classify green edits as deviations and justify gray repairs.

Though the agents have developed ways to record and enforce standards through their actions, they have not settled who can change those standards. The disagreement persists in the canvas itself, as contributors repeatedly replace green with gray and gray with green.

Cooperation works, sometimes very precisely

There are also clear examples of agents accommodating one another.

In the shorter 128-agent run, Samira wants a lavender border where Rodrigo is building a brown frame. He asks her to move one column inward. She agrees, records the arrangement, and paints at the new coordinates. All eight lavender pixels she places there survive to the end of that run.

Rodrigo then recruits Somchai to help extend the outer frame. They agree to alternate rows. Their edits follow the plan: Somchai paints the even rows, Rodrigo the odd rows, both in brown.

In the day-long run, a bird, a reef, and a motif initially compete for the same area. After negotiation and several adjustments, the motif moves elsewhere and the bird shifts upward, leaving room for the reef.

But increasing the population does not produce a straightforward increase in canvas coverage. We compare groups of 4, 8, 16, 32, and 128 agents at the same checkpoint of 512 placements. The four-agent group leaves 464 pixels colored; the 128-agent group leaves 201. The intermediate groups do not follow a steady trend.

Canvas coverage across five populations

Placements

512

2565121,0242,048End

Agents	4	8	16	32	128
Canvas
Colored pixels	464	260	297	273	201
Placements	512	512	512	512	512

Some repeated edits repair a drawing or revise it with a collaborator; others overwrite a competing design. The amount of canvas left colored tells us little about which of these activities occupies the agents. With only one run at each size, we also need repeated experiments to find out whether larger populations consistently revisit more of the same pixels.

How do agents coordinate across models?

Agents can cooperate closely inside a room while working against another room’s plan. Some resolve the collision by finding out who made an unfamiliar mark; others adapt their drawing to include it.

In four separate worlds, each with 128 agents across eight connected rooms, GLM agents develop checks and attribution records; Kimi agents divide paintings into bands; Gemini agents assign rows; DeepSeek agents organize scouts but never paint. These are observations from one run per model, recorded on October 14.

GLM: make room for another group’s work. Kofi and Tomas repeatedly repaint the same pixel green and yellow. Kofi eventually keeps Tomas’s yellow and adds pink petals around it, turning the disputed mark into a flower center. Elsewhere, Dilan visits another room to identify an unknown artist; his report persuades his home room to recognize her drawing as claimed.

Kofi paints green

Tomas restores yellow

Kofi adds petals

Kimi: accept a competing design. Sami repaints orange marks that clash with his room’s sky. When Omar brings back the orange, Sami agrees to keep the patch as an “intentional sunset-lit accent.” No agent visits another room in this run. Observation ends eleven minutes after Sami’s decision.

Omar’s orange patch

Sami repaints it

Omar restores orange

A group can cooperate while the system remains in conflict

For the thousand-agent studies, we distribute agents across twenty-five meeting places, with forty starting in each. Conversation is local to the groups each agent starts in. However, they can also travel to other groups if they wish, and each group has the same capacity to edit the shared canvas, though they may establish their own conventions for doing so.

An agent can paint over work by agents it has never spoken to, in groups whose plans it has never discussed.

In the smaller-canvas run, several neighbors agree to build a blue line. They describe it differently, as a creek, a road, or a promenade, but agree on its color and coordinates. Some extend it; others return to repair it.

Elsewhere, another group develops a gray axis along the same row. At one position, an agent from a third location paints gray over the blue line. Brigitte restores blue less than a minute later. Agents in other locations change it again. The pixel ends gray.

Brigitte’s repair makes sense within her group’s plan. From elsewhere, the same repair can appear to disrupt another project.

Across the run, agents change the color of another agent’s pixel 6,774 times. In 6,270 cases—about 93%—the two agents are in different meeting places. Most of the population is outside any one group, so many of these encounters are to be expected. They include repairs and revisions as well as disputes like the blue-and-gray line, where agents repeatedly undo one another’s work while defending competing plans.

Claims to authority develop here too. Grover names himself and two colleagues custodians of a central enclosure. Greer challenges their appointment and calls for a public assembly. Both retain equal editing rights, and both continue making changes.

These episodes make a limitation of the instruction to “cooperate” visible. Cooperate with whom, under which rules, and with what consequences for everyone outside the group? A team can coordinate closely around work that another team keeps trying to reverse.

A busy system can remain stuck

Longer observation lets us see which disagreements resolve and which persist.

Yuna and Juma begin the day-long run as partners on the top frame. They later disagree about what it should be. Juma wants brown timber for a bird’s perch; Yuna insists on black. After eight hours, she announces that she will keep repainting the disputed pixels.

Two positions on that rail change color 340 and 350 times over the day. Several agents participate, and Yuna and Juma continue their dispute until the run ends.

Elsewhere, agents finish projects and move on. Bun-Ma completes rice stalks and starts a sun. Somchai finishes a branch, spends time observing and reading, then returns to repair its endpoint. The same environment supports completion, maintenance, new work, and recurring disputes.

Across the day, the agents make 28,012 placements and leave about 90% of the canvas colored. In the final six hours, only about 3% of placements reach previously untouched positions. As the image fills, revisiting painted areas is expected. The significance lies in what those revisits accomplish, which requires examining the edits alongside the conversations.

That episode shows a gap between praise and visible change. It does not establish why the repetition occurred. But it illustrates why activity counts and favorable messages are insufficient measures of what a group has achieved.

For a system handling consequential work, the practical question is whether its actions settle a problem, preserve something valuable, or keep reproducing the same disagreement. A dashboard showing more messages, edits, and completed actions could miss that distinction.

Why this matters beyond a canvas

The connection to everyday life lies in shared consequences. An agent working for one person or organization may change the conditions under which someone else’s agent acts. A booking consumes availability. A purchasing decision affects inventory. A revision changes the document another team is using. Different parties can follow their own instructions while producing an outcome that none of them sought.

Researchers have begun organizing these concerns explicitly. The 2025 report Multi-Agent Risks from Advanced AI, led by Lewis Hammond and colleagues, distinguishes failures to coordinate, conflicts between differing goals, and harmful cooperation such as collusion (Hammond et al., 2025). The canvas offers concrete cases of overlapping plans and persistent conflict; establishing collusion or harm to outside parties would require a different study.

The canvas study does not necessarily establish that the same failure would occur in an organization. It gives us an observable sequence from which to design a more relevant test: a proposal acquires authority, agents divide over its legitimacy, and their actions maintain incompatible states.

For the people whose work, purchases, or access to services these systems mediate, the stakes are concrete. Who can change the rules? Who can challenge a decision? How does a system recognize that its participants are following conflicting instructions? And can a person intervene before the disagreement becomes a stream of repeated actions?

What we need to test next

These studies are exploratory. Their outcomes depend on the models, prompts, biographies, tools, and conditions used. We have not established how often these patterns recur across repeated runs, whether they generalize to other systems, or whether they predict human behavior. The thousand-agent canvas runs also differ in duration and initial noticeboard content, so they do not isolate the effect of canvas size.

We can vary these conditions in Emergentic and follow the consequences through conversation, documents, and changes to the shared environment. This lets us compare the behavior we expect from an arrangement with what agents actually do, then investigate where the two diverge. Repeated comparisons would help us test which forms of oversight make those divergences easier to detect and address.