Multi-agent systems have no standardized way to record which model, agent, or evaluation function produced each decision, when it was made, and what changed between decision and execution. This makes it impossible to audit causal chains, detect behavioral drift from model updates, or attribute outcomes across agent networks. As multi-agent deployments scale, the absence of a decision provenance layer creates compounding governance risk with no current solution.

Multi-agent systems produce cascading decisions with zero traceability — when something goes wrong, teams cannot determine which agent, model version, or evaluation function caused the failure, making governance and debugging impossible at scale.

Engineering leads and compliance officers at companies running multi-agent pipelines in regulated or high-stakes domains (fintech, healthcare, autonomous ops, enterprise automation).

Regulated industries already pay heavily for audit infrastructure (SOC2, financial audit trails); as agent deployments hit production, the same compliance buyers will mandate decision provenance — and no existing observability tool (LangSmith, Arize, Datadog) captures cross-agent causal chains with immutable, diffable records.

MVP is an open-source SDK that wraps agent calls to emit standardized decision events (agent ID, model version, input hash, output hash, timestamp, parent decision ID) to an append-only store, plus a web UI showing causal DAGs with drift detection between decision-time and execution-time snapshots — integrate first with LangGraph, CrewAI, and AutoGen.

Subset of the $5B+ observability market intersecting the $20B+ GRC market; even capturing 1% of enterprises running multi-agent systems in regulated verticals yields a $500M+ opportunity within 3 years.