Beyond AI Agents: Building the CX Control Plane

2026-05-15 · Source: Opus Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, quick

Summary

Opus Research and Cyara are hosting a free webinar on June 17th, 2026, titled "Beyond AI Agents: Building the CX Control Plane," addressing the complexities of managing multiple AI agents in Customer Experience (CX) organizations. The session will tackle challenges like coordinating numerous agents, establishing rules, and ensuring their intended performance. Presenters will detail Opus Research's five-layer control plane model, with a deeper dive into the evaluation and policy layers, which are critical for production environments. Key discussion points include the inadequacy of traditional QA in multi-agent setups, designing cross-channel evaluation frameworks, implementing scalable policy guardrails, and defining practical "trusted CX automation." This event aims to provide solutions for maintaining accountability and observability in multi-agent CX systems.

Key takeaway

For Directors of AI/ML overseeing multi-agent CX deployments, understanding governance beyond initial agent launch is crucial. Your current QA and testing approaches likely won't scale, risking uncoordinated or misaligned agent behavior. Attend the June 17th webinar to learn how to implement Opus Research's control plane model, specifically focusing on evaluation and policy layers, to ensure your AI agents remain accountable, observable, and continuously improvable. This will help you build trusted CX automation.

Key insights

The core challenge in CX is governing, evaluating, and setting policy for multiple AI agents, not just deploying them.

Principles

• Traditional QA fails in multi-agent CX.
• Policy guardrails must scale without hindering innovation.
• Trusted CX automation requires robust governance.

Method

The webinar will walk through Opus Research's five-layer control plane model, focusing on evaluation and policy layers for multi-agent CX environments.

In practice

• Design evaluation frameworks for agents and channels.
• Implement policy guardrails for multi-agent systems.
• Establish accountability in CX automation.

Topics

• AI Agents
• CX Control Plane
• Multi-agent Systems
• AI Governance
• AI Evaluation
• Policy Guardrails

Best for: AI Architect, CTO, VP of Engineering/Data, Director of AI/ML, MLOps Engineer, Operations Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by Opus Research.