Replacing Supervisor LLMs with Typed State Machines Cuts Multi-Agent Token Use by 70%

Engineers building multi-agent AI systems often rely on a central supervisor LLM to coordinate specialized subagents, but this approach inflates token costs, causes evaluation drift, and leaves poor audit trails. The core problem is that supervisor LLMs re-evaluate growing context windows at every step, causing token consumption to scale quadratically with workflow depth. To address this, the team replaced their supervisor LLM with a deterministic typed state machine, where each agent step operates on a strictly defined input payload and returns a schema-validated receipt instead of free-form text. This architectural shift eliminated non-deterministic routing loops and reduced overall token consumption by over 70%. The typed receipt system also creates a verifiable transition log, making it straightforward to audit why any specific agent action was taken.