Multi-Agent AI Systems Often Fail From Poor Coordination, Not Weak Agents

A DEV Community article argues that AI agents working together can perform worse than a single agent when they lack coordination, with duplicated searches, conflicting rewrites, and looping calls driving up costs. The author compares this to disorganized college group projects, where overlapping and contradictory edits undermine the final result. The proposed fix is not removing agents but adding simple handoff rules, such as requiring agents to claim tasks before starting and allowing only one agent to write each result. A minimal coordination contract in Python is offered as an example, using a dictionary to track task ownership without machine learning or vector databases. The author recommends rules that let agents disagree by creating new revision tasks rather than overwriting others' work.