Your AI-generated post-mortem is just a fancy way of hiding the truth
I read a post on Lobste.rs last week about dreading our LLM-written incident report future, and it hit a massive nerve. We've reached a point where the moment production stops burning, our next instinct's to automate the thinking. I'm tired of reading beautifully formatted incident reviews that read like they're written by a consultant who's never seen a terminal. They've all the right sections,…
The article titled "Your AI-generated post-mortem is just a fancy way of hiding the truth" argues that the use of artificial intelligence to write incident reports for software engineering teams is a superficial approach that ultimately undermines organizational learning. The author recounts a specific incident that occurred in their own organization, describing how a seemingly minor change to their caching logic led to a massive cache stampede across all product pages.
This incident caused significant chaos as the team struggled to diagnose and resolve the problem, with multiple people attempting to implement different recovery scripts simultaneously.
The author emphasizes that AI-generated incident reports tend to gloss over the messy, human aspects of post-mortems, such as confusion, false starts, and miscommunication among team members. These are precisely the elements that provide valuable lessons for future incidents. By relying on AI to craft clean, well-structured reports, teams risk losing the authentic insights that emerge from honest, candid discussions about what went wrong.
The author warns that treating post-mortems as mere compliance exercises, rather than genuine opportunities for reflection and improvement, ultimately undermines the very learning they are meant to facilitate. They assert that organizations must resist the temptation to outsource their collective understanding of systems to automated processes, and instead prioritize capturing the full, imperfect truth of incidents through human-led review and analysis.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
