Bug Fixing Is an ETL Problem (qckfx.com)

Chris Wood, founder of qckfx, argues that the hardest part of fixing bugs isn’t writing patches but the ETL-like work of collecting, correlating, and interpreting the vast “derivative” data produced by code execution—logs, errors, traces, metrics and database state from tools like Sentry, Datadog, Stripe, etc. He frames debugging as an iterative data-pipeline problem: each monitoring product refracts the original execution differently, and the real task is extracting, transforming and joining that evidence to surface the causal path to a defect. Reading code alone—even with LLM help—is often insufficient; you need instrumentation, reproductions and bisects to generate the right signals. That framing makes debugging a natural fit for LLM-driven agents. qckfx is building AI agents that orchestrate searches across code, logs, traces and DB state, spawn browser/coding sub-agents to run reproductions and bisects, and then propose a concrete remediation path—turning hours of manual correlation into minutes. For the AI/ML community this highlights a shift from using LLMs purely for code generation to using them as orchestration and ETL engines that integrate heterogeneous telemetry. Technical implications include the need for rich connectors, reliable instrumentation, human-in-the-loop verification, and metrics for agent accuracy and safety; qckfx already offers GitHub, Slack and Sentry integrations and a trial for early users.