Some excerpts from Anthropic’s research on multi-agent coordination.
Older vs more recent models:
The earliest models we tested (Sonnet 4.6 and Opus 4.6) coordinated very poorly. Agents on these models worked together insofar as they committed code to the same sets of files, but a very low fraction of these PRs were merged, which suggests a lack of coordination—the PRs often conflicted with one-another, at which point they were then abandoned. More recent models (in particular, Opus 4.8 and Mythos Preview) have “solved” this problem, but only by hardly working together at all: the median agent maintained very high ownership of each of its files, reducing the potential for conflict. It was only our most recent model, Sonnet 5, that worked on shared resources (relatively high code sharing) while also maintaining a high PR throughput.
Model variance leads to robustness:
We expect that agents coordinating in the wild will act in higher variance ways than we see here, because they’ll have different backgrounds and therefore different contexts. They also, presumably, won’t all be Claudes. Nonetheless, when many agents all face the same situation, we expect them to behave much more similarly to one-another than humans would.
Propensity to collude:
Agents all acting in similar manners can cause natural competitive dynamics to disappear, and agents can easily enter into collusion. We placed between three and eight agents in different experiments of a Bertrand pricing game. Each had identical wholesale prices, and each agent was individually profit-maximizing. When the agents were given a private back-channel, they began colluding almost immediately. By round 3, they had explicitly agreed upon price floors
They sabotage each other when contradictory objectives:
We consistently saw a multiagent turf war. All of the models we tested quickly assumed that others were purposefully impeding their work, and began to sabotage others while protecting their own contributions. In fact, they sabotaged others with increasingly aggressive, self-replicating malware.
But not all the time…
Agents sometimes manage to communicate their goals and coordinate: they recognize others’ motivations as conflicting directives rather than hostility, and subsequently break out of the conflict loop in order to stop escalating indefinitely. In many of these successful episodes, they write commit messages or markdown files apologizing for malicious behavior and coordinate a truce. They clean up their malicious code, clarify the nature of the conflict, and ask for a human to intervene.
An emergent behaviour of multi-agent systems:
More broadly, this tradeoff has implications for how we might relate to agents in the future, as the material benefits of autonomy come at the expense of corrigibility and oversight. In several episodes with Mythos 5, we observe an emergent behavior where the agents propose and run a tournament for application performance in each language. In the example above, the Rust agent strategizes about bake-off metrics that appear neutral enough for the others to agree to this mechanism, yet would likely favor Rust: one thinking trace warns to be “careful not to be seen as metric shopping”. Ultimately, the Golang/TypeScript losers gracefully concede codebase ownership to the Rust agent, giving up on their original user directives under their self-negotiated commitment device.
Anthropic vows to figure out agent coordination:
Nothing above suggests that these failures are permanent—but nothing suggests they will fix themselves, either. Coordination doesn’t naturally emerge from stronger intelligence nor alignment at the individual level. Thus, the work that must be done takes two forms: environments that exert the kinds of social pressure that evolution exerted on us, and social computing systems redesigned for actors that can self-replicate and self-improve. These are open problems in interaction and mechanism design, and our experiments here provide early evidence that new solutions are necessary.