A data engineer recounts building a two-agent production system for incident triage, where prompt-based agents investigate failures in parallel across separate repositories, coordinate to find where a fix belongs, and draft pull requests or tickets for human review. The piece details the architecture (agents hooked into PagerDuty, GitHub, CloudWatch, Datadog), the role of versioned skill files pulled from GitHub, and hard-won guardrails: human-in-the-loop review, scoped permissions, observability into agent reasoning, and shadow-mode evaluation before trust. It argues agents excel at failure triage, team-conventioned code generation, and lineage/schema-drift tracing, but fail without a governed metadata layer, and that the real shift is engineers moving up to platform architecture and context-curation work rather than being replaced.
Table of contents
What an Agent Actually IsWhy Data Engineering Is Hitting a WallWhat We Actually BuiltWhere Agents Help, and Where They Don'tGuardrails for ProductionWhat Actually ChangesWhere to StartConclusionQuestions this post answers
How do you use AI agents for data pipeline incident response in production?
A working setup uses two agents that each own a separate repository; when an alert fires via PagerDuty, both investigate logs, metrics, and recent changes in parallel, then coordinate to resolve which one owns the fix, avoiding race conditions through a handoff mechanism. The owning agent then drafts a pull request or ticket with context filled in, which a human reviews before it lands. See how teams pair AI coding agents with production guardrails by following agentic workflows on daily.dev.
Why do AI agents give wrong answers when investigating data pipeline failures?
Agents hallucinate and misroute fixes when the underlying metadata and lineage context is incomplete or stale, proposing fixes for the wrong table with full confidence. The reliability of an agent depends entirely on the trustworthiness of the context layer beneath it, meaning weak data governance undermines any agent built on top of it, regardless of how well the agent itself is designed. Engineers weighing AI agent reliability against data governance gaps can track these lessons on daily.dev.
What guardrails should you put on autonomous AI agents that touch production data?
Require human review on anything that writes or changes state, so every pull request an agent drafts gets approved by a person before merging. Scope agent permissions tightly so it can read logs and file tickets but cannot drop a table, maintain observability into its reasoning trace and tool calls, and run it in shadow mode comparing its output against human findings before trusting it live. Teams designing safe AI agent guardrails for production systems can follow similar practices on daily.dev.