Honeycomb went from 30 to 70 merged pull requests a day in three months. The catch: automated code review, not code generation, became the real bottleneck of software automation. Liz Fong-Jones, Technical Fellow at Honeycomb, explains why incidents still rose 1.5x, how their internal bot Autobot now reviews every PR, and why AI amplifies whatever org you already have.
What we cover:
– How automated code review lets humans focus on design, not trivial bugs
– Using a decision model like Jev to decide which PRs are safe to auto-merge
– What makes a codebase ready for AI coding agents
– When to trust AI agents with production incidents, and when they're just throwing darts
– Why "Claude did it" isn't an excuse, and what ownership means with AI
– How open source maintainers can handle a flood of AI slop pull requests
Chapters:
00:00:00 - Introduction
00:06:41 - Why AI amplifies dysfunctional engineering orgs
00:10:45 - What makes a codebase AI ready
00:14:01 - Honeycomb's Autobot and automated code review
00:19:06 - Using Jev to decide which PRs are safe to merge
00:26:51 - Trusting AI agents during production incidents
00:30:40 - Least privilege and guardrails for coding agents
00:33:25 - If your name's on it, you own it
00:38:55 - AI slop pull requests and open source
00:45:56 - Will observability engineering survive as a role?
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