Building a performance-specialized AI agent requires teaching LLMs to interpret complex telemetry data from Chrome DevTools trace files. The journey involves segmenting trace data into focused problem spaces, using specialized sub-agents with narrow objectives to overcome context window limitations, and implementing workflow-based architectures inspired by Mixture of Experts. The Model Context Protocol enables orchestrating agents to delegate performance analysis to DevTools' internal agents, creating automated workflows for profiling and course correction. Success depends on careful context engineering, keeping sessions focused, and maintaining human verification of AI-generated insights.
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