Official Session Summary
Pulled from the live conference page.
As AI agents become increasingly capable, a critical architectural question emerges: how should we give them access to tools and capabilities? Two competing patterns have gained traction—Model Context Protocol (MCP), which enables dynamic, runtime tool discovery and execution, and the command line, including skill files, which embed structured instructions and best practices directly into the agent's context. But which approach actually produces better outcomes? In this talk, I'll present results from a rigorous evaluation comparing agent performance across both paradigms. I'll show how each approach affects task completion rates and output quality across document generation, data analysis, and multi-step workflows.
Speaker Background
Quick context on the person or people on stage.
AI Engineer / Head of DevRel at Arize AI, with a strong systems and evaluation lens on tooling patterns, observability, and agent performance.
Why This Slot Matters
A compact framing layer for navigating the conference.
This is one of the more substantive abstract-backed sessions on the schedule; worth opening when you need enough context to decide whether to stay in the room.