AI Norms & Values, Part 2 of 3: AI for Honeycomb Engineering

Honeycomb’s engineering leadership reflects on its year-long AI adoption mandate, arguing that AI is essential for shaping industry practices, maintaining credibility as an observability expert, and helping customers move beyond outdated software-development approaches. The organization’s north star is to become among the top 10% most AI-enabled and productive engineering teams at comparable companies, alongside a company-wide goal of doubling impact through AI. Near-term objectives include safely auto-merging at least 25% of AI-reviewed pull requests while keeping change failure rates below 3%, maintaining service-level objectives and sustainable incident workloads, adapting work to remain meaningful and sustainable, and publicly sharing lessons learned. Honeycomb emphasizes that AI should support the entire software development lifecycle rather than merely accelerate coding, with employees expected to experiment, develop shareable insights, and work with managers to assess performance rather than meet fixed individual usage targets. The guidance supports exploration of agentic workflows and possible software-factory models without prescribing exact practices, while encouraging engineers to use discussions productively, avoid unproductive Slack debates, and acknowledge the professional and emotional adjustments that accompany more autonomous AI tools.

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