MUSE

MUSE is a verification and control layer for tool-using AI agents. Agents can call tools, change external state, and report success — but an accepted action is not necessarily a correct or completed action. Evidence can be incomplete, approvals can target the wrong resource, and a successful API response can still be followed by a failed real-world state. MUSE sits between model intent and external state and verifies the entire workflow: Evidence → Consistency → Policy → Action → Receipt → Readback → Verified State. It enforces evidence completeness, scoped approvals, idempotent execution, and authoritative post-action readback. We tested the same verification engine across synthetic Marketing, E-commerce, DevOps, Mechanical Engineering, and Inventory workflows. On five workflow-critical synthetic benchmark cases using the same configured model, tools, scenarios, limits, and frozen evaluator, the baseline passed 0/5 while MUSE passed 5/5 with 0 hard failures. The full regression suite passes 176/176 tests. MUSE is not another agent. It is the layer that makes agent actions verifiable.