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6 posts · honeycomb.io
Honeycomb’s engineering leadership outlines why the org is “all‑in” on AI, sets a north‑star to be in the top 10 % AI‑enabled teams, and publishes concrete 2026 goals (e.g. 25 % of PRs auto‑merged by AI with <3 % failure) plus an FAQ covering support, measurement, agent usage, and coping with workflow changes.
Fin’s CTO describes how an AI‑powered PR review system, combined with strong observability, helped the company nearly triple engineering productivity, driven by a public goal and a repeatable learn‑do loop.
The article compares seven Datadog alternatives for AI and agent observability, focusing on investigation capabilities, telemetry economics, and OpenTelemetry support. It highlights Honeycomb’s event model, pricing model, and a case study showing 50% cost savings.
Honeycomb’s first AI‑norms document (How We Do Business) codifies eight concrete GTM principles – value‑first, human‑centric, truthful, earned‑asks, hard‑but‑fair competition, respect for attention, AI‑augmented responsibility, and durable relationships – and argues that AI merely amplifies existing culture rather than creating new rules.
An engineering manager shares her approach to integrating AI tools into her team's workflow by creating dedicated spaces for connection, collaboration, and discussion, rather than imposing new policies. This strategy fostered psychological safety and autonomy, allowing the team to organically explore and define their own AI norms and practices.
Honeycomb’s guide explains the four main kinds of AI model drift (data, concept, upstream, and prompt/embedding/output), why drift is hard to spot in LLM‑based systems, and how to set up baselines and observability signals (distribution stats, evaluation scores, user feedback, retry rates, etc.) to catch it early.
