Multi-Agent Data Alignment
Multi-agent AI networks don’t have a data problem — they have an alignment problem. Multi-Agent Signals (MAS) is the coordination layer that sits between concurrent agent fetches and every downstream decision, payment, or synthesis output: detecting conflicts, enforcing delegation scope, and weighting reputation before anything propagates. Real economic settlement such as Circle’s x402 and Arc L1 means alignment quality has real financial consequences in a real world environment that will touch billions globally.
Full alignment & conflict check per run
Deterministic, auditable conflict adjudication
Alignment quality determines USDC payout
Zero-Knowledge output integrity verification
Multi-Agent Signals (MAS) instruments every alignment run as a structured research record: conflict type, agents involved, freshness status, confidence deltas, reputation changes, and settlement outcome. These records support empirical study of how trust degrades, how collusion patterns evolve under economic pressure, and how alignment failures propagate to downstream synthesis — across any domain where competing principals contribute data.
