AI Amplifies Existing Practices: Lessons from Our AI Shift

Blog

June 18, 2026

The Second Edition of Observability Engineering Is Here

The second edition of Observability Engineering is available for download on our website.

In this two-part blog series, I give a detailed report-out on how our Honeycomb engineering team 2.5x-ed our throughput using AI without breaking everything or lowering our standards for quality.

The “platform engineering” frame and the “autonomy, ownership, feedback loops” frame are the same frame, spoken in two different vocabularies. AI amplifies your existing practices. It can make a dysfunctional org more dysfunctional, or it can bring out the best in an org that has high autonomy, ownership, and feedback loops.

Autonomy is the ability to ship without asking permission. Continuous delivery, hermetic builds, feature flagging, fast CI: this is the substrate that lets work flow...

Continuous delivery: Bound the duration of any individual bad PR

We run an hourly deploy train. Twelve to fourteen deployment events on a typical workday, packing about 70 PRs across them, with one to three reverts. Recovery is within the hour, because the next train is right behind. This bounds the duration of any individual bad PR’s impact in production...

CLAUDE.md and skills: Code ownership reframed

DORA-style code ownership was about authorship: who wrote the code, who’s on the hook to maintain it. With agents writing the majority of new lines, authorship is no longer load-bearing...

Auto PR review, with humans empowered to push back

Auto-review agents catching style violations and obvious holes are useful, and they don’t substitute for senior engineers willing to say, “This is slop, this is waste, this is over-verbose, this is missing tests,” out loud and in writing...

MCP: Give the agent the tools the human had

In a single Claude Code session or Slack thread (pointing autobot at the issue), an engineer can file a Linear ticket, fix the issue, and submit a PR pointing back at the ticket...

Fast and AI-legible CI

Two things have to be true for CI to function as part of an agent’s feedback loop. It has to be fast, and it has to be AI-legible...

Closed-loop observability for agent work

The trap to avoid is treating “agentic observability” as monitoring the agent itself. Tokens spent, sub-agent decisions, internal flow: these are interesting, and they’re not the point...

AI for slop cleanup, not just generation

When you buy a table saw, you don’t keep doing your woodworking the way you did with a hand saw...

The dissemination layer

Code review does two jobs: it catches bugs, and it produces shared understanding among humans about what’s changing...

Feature flagging and SLOs

Feature flagging sits at the intersection of autonomy and feedback loops...

Where the skeptics are right

Two real concessions belong here...

Order of adoption

This is the question we get asked most: which platform practices to adopt, in which order...

Structuring the platform team

In an AI-amplified world, the platform team’s job grew...

What’s left if you read nothing else

AI amplifies your existing practices. Going fast without autonomy, ownership, and feedback loops is enshittification...

What we’re watching

The bottoms-up process for “what we’re going to measure next” is still landing inside the org...

AI Influence Level disclosure

This post: AIL-3.0 (substantial AI involvement, human steering on every load-bearing call)....

Sources and context: Fin/Intercom 2x post; Fin/Intercom AI PR approval safety post; Honeycomb-Intercom case study; Emily Nakashima on AI-amplified engineering leadership.

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