Why zkML? Because the enterprise AI orchestration company CallSine just introduced the first platform that converts Customer Data Platform (CDP) data directly into orchestrated, deterministic AI agents — enabling end-to-end actions across sales, marketing, and customer operations. When agents begin shaping GTM execution autonomously, verifying why each action was triggered becomes essential.

2/ This system doesn’t simply trigger automations. It transforms customer data into structured signals that agents can follow step-by-step: From analyzing history, detecting intent, to executing coordinated workflows without human routing. But once execution is delegated to agents, the challenge isn’t scale — it’s justification.

3/ The issue: CDP-based agent workflows easily become opaque. Teams often see the outcome, but not: -Which customer signals initiated a workflow -Whether the approved orchestration path was followed -Whether the model applied the correct logic -Whether any drift or misrouting occurred across agents For enterprise operations, unverified reasoning becomes a systemic risk.

4/ That’s where zkML fits: ✔️Prove each agent action came from the approved model ✔️Prove which criteria guided the decision — without exposing customer data ✔️Prove orchestration rules were followed exactly ✔️Enable auditors and ops teams to validate reasoning without leaking internal logic zkML shifts agentic automation from “autonomous execution” to “provable execution.”

5/ Agentic orchestration isn’t just about automation — it’s about accountable execution. And CallSine’s move to anchor deterministic agents directly on top of a CDP marks a new category: Customer data becoming a verifiable decision engine. At that frontier, proof becomes part of the operational stack. That’s what @PolyhedraZK is building: AI systems where every autonomous action can justify itself.