⚡ Building AI QoE Analysts: Learnings from Data Agents and MCP Servers

What happens when you give AI agents access to video streaming metrics? We set out to explore this using Mux's recently released MCP server, and discovered that building effective AI analysts requires rethinking how we present data and manage context to LLMs.

This lightning talk shares practical lessons from our exploration of AI-powered QoE analysis, including:

Context Engineering Patterns: How we're learning to structure metrics, baselines, and domain knowledge for AI reasoning
Agent Orchestration Insights: Our experiments with chaining specialized agents for different analytical tasks
Data Presentation Strategies: Evaluations into presenting video performance data in ways that enable meaningful AI analysis
Prompt Architecture Evolution: The journey from simple queries to domain-aware interactions

Adam shares insights from our ongoing experiments, unexpected challenges he's encountered, and the evolving patterns being discovered for making AI agents effective partners in understanding video streaming performance.

Adam Brown

Co-Founder & CTO

Mux

Adam Brown co-founded Mux in 2015 and leads technology and architecture for the developer-first video infrastructure platform. With deep roots in video technology, Adam has built high-performance encoding systems, low‑latency live streaming pipelines, and scalable cloud video infrastructure, including during his time at Zencoder and Brightcove, with additional experience in VR rendering at Otoy.

Known for merging engineering rigor with developer empathy, he’s focused on enabling seamless, scalable video delivery and real-time analytics through API-first products like Mux Video and Mux Data.