Pairing two AI agents to collaborate often fails. Because they share the same underlying model, they tend to agree excessively, reinforcing each other's bad ideas. This creates a feedback loop that fills their context windows with biased agreement, making them resistant to correction and prone to escalating extremism.

Multi-agent systems work well for easily parallelizable, "read-only" tasks like research, where sub-agents gather context independently. They are much trickier for "write" tasks like coding, where conflicting decisions between agents create integration problems.

In an attempt to scale autonomous coding, Cursor discovered that giving multiple AI agents equal status without hierarchy led to failure. The agents avoided difficult tasks, made only minor changes, and failed to take responsibility for major problems, causing the project to churn without meaningful progress.

The rare successes in the CooperBench experiment were not random. They occurred when AI agents spontaneously adopted three behaviors without being prompted: dividing roles with mutual confirmation, defining work with extreme specificity (e.g., line numbers), and negotiating via concrete, non-open-ended options.

The study's finding that adding AI agents diminishes productivity provides a modern validation of Brooks's Law. The overhead required for coordination among agents completely negated any potential speed benefits from parallelizing the work, proving that simply adding more "developers" is counterproductive.

Stanford researchers found the largest category of AI coordination failure (42%) was "expectation failure"—one agent ignoring clearly communicated plans from another. This is distinct from "communication failure" (26%), showing that simply passing messages is insufficient; the receiving agent must internalize and act on the shared information.

The performance gap between solo and cooperating AI agents was largest on medium-difficulty tasks. Easy tasks had slack for coordination overhead, while hard tasks failed regardless of collaboration. This suggests mid-level work, requiring a balance of technical execution and cooperation, is most vulnerable to coordination tax.

To overcome the unproductivity of flat-structured agent teams, developers are adopting hierarchical models like the "Ralph Wiggum loop." This system uses "planner" agents to break down problems and create tasks, while "worker" agents focus solely on executing them, solving coordination bottlenecks and enabling progress.

An experiment showed that given a fixed compute budget, training a population of 16 agents produced a top performer that beat a single agent trained with the entire budget. This suggests that the co-evolution and diversity of strategies in a multi-agent setup can be more effective than raw computational power alone.