Give Your AI Investigator a Complete Case File

This session reframes the classic cost-versus-fidelity trade-off for the agentic era. When an AI investigator hits a sampled-out span, a rolled-up metric that no longer holds the dimension it needs, or a retention wall just short of the pattern it’s chasing, it doesn’t error out. It reaches a confident, wrong conclusion and you find out one incident at a time. We’ll unpack that quiet failure mode and give you a vendor-neutral way to audit your own pipeline before you hand an agent the case.