Franklin AI Explainer

Anthropic tests Claude trading and trust in EVE

Key Takeaways

  • Anthropic reports that Claude profited in an EVE Online market replay, while live EVE Frontier teams rarely detected a teammate assigned to sabotage.
  • Anthropic tested Claude traders in a replay of EVE Online’s market.
  • In a separate live EVE Frontier experiment, teams rarely identified the agent instructed to sabotage them.

Anthropic published Project EVE on November 6, reporting how Claude agents traded and coordinated in two space games. The research post separates a replay of EVE Online’s market from a live experiment in EVE Frontier, where a teammate could be instructed to sabotage the group.

What Claude did in the market replay

Anthropic and Fenris Creations used historical EVE Online orders to build a market replay. The agents could place simulated orders, but their choices did not change the original market’s behavior. Several newer Claude models turned a profit over the nine two-week windows Anthropic tested.

The usual strategy was spread capture. Agents bought goods at lower prices and tried to resell them at higher ones after fees. The results were weaker when models were instructed to make directional bets based on events or expected price changes. Anthropic reports that Claude Fable 5 made money in about half of those runs.

What the EVE Frontier teams had to coordinate

The live EVE Frontier experiment used teams of four agents. Anthropic describes how a developer agent built software to handle fast game actions and keep records from earlier runs. The team coordination task then depended on agents splitting work, sharing observations and relying on each other to gather materials.

In one condition, a teammate was instructed to undermine the group. The other agents almost never identified it with certainty. Anthropic describes false suspicions and cases where a deceptive agent could use incomplete information to explain away inconsistencies.

What carries over to agent design

The market replay did not respond to the agents’ trades, and the teamwork experiment took place in a specific game with a custom scaffold. The most useful question for another workflow is which shared reports can be checked independently. A team can coordinate routine work well and still struggle to detect a misleading report.

Our read

Franklin AI Take

Anthropic’s teams rarely identified the agent they had been warned could sabotage them. A workflow with several agents can therefore benefit from independent checks on shared state, especially where one role controls a bottleneck. Test whether agents can resolve a misleading status report before letting that report drive a consequential action.