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.

Emmett Shear highlights a critical distinction: humans provide AIs with *descriptions* of goals (e.g., text prompts), not the goals themselves. The AI must infer the intended goal from this description. Failures are often rooted in this flawed inference process, not malicious disobedience.

Having AIs that provide perfect advice doesn't guarantee good outcomes. Humanity is susceptible to coordination problems, where everyone can see a bad outcome approaching but is collectively unable to prevent it. Aligned AIs can warn us, but they cannot force cooperation on a global scale.

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.

Humans mistakenly believe they are giving AIs goals. In reality, they are providing a 'description of a goal' (e.g., a text prompt). The AI must then infer the actual goal from this lossy, ambiguous description. Many alignment failures are not malicious disobedience but simple incompetence at this critical inference step.

Today's AI agents can connect but can't collaborate effectively because they lack a shared understanding of meaning. Semantic protocols are needed to enable true collaboration through grounding, conflict resolution, and negotiation, moving beyond simple message passing.

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.