sprix-sage-router

SAGE decides which agent should do the next piece of work after execution has already started. Agent discovery tells a system who exists; it does not answer whether the agent currently working should continue alone, recruit a collaborator, or hand the whole task off. SAGE scores those three routes in one objective under permission, budget, and deadline constraints. MIT, Python, and it sits above the A2A protocol rather than replacing it.

The design choices are more interesting than the pitch. Trust is learned per agent and per requirement rather than as one reputation score, so strength at coding does not imply strength at research. Teams get credit for covering the requirements still outstanding, not for collecting high-ranked but redundant agents. Every decision emits assignments, topology, cost, latency, risk, and a readable rationale.

Free with nothing above it. The costs are the model calls your agents were already making, plus routing overhead from scoring candidate teams before each decision.

The catch is that this is research output, not production infrastructure. It was published in August 2026 by a small lab, the benchmarks are its own, and the learned outcome model is described as a stand-in for a real reward model later. Read ALGORITHM.md before wiring it into anything that matters.