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Charity Majors
In an open letter to engineering leaders everywhere, Fin CTO Darragh Curran explains that AI isn't a magic wand but rather an amplifier—of the good and the bad—of your engineering practices. And engineering rigor is more important than ever.
Dan Juengst
Colin Burke
Reid Savage
Josh Parsons
We just completed a large-scale, multi-month Kafka migration project. We couldn't have done it without learning from past mistakes, prioritizing rollback safety, and building shared knowledge across the team through repeated migration practice.
Juliana Gomez
In the past, I was part of a group of engineers responsible for doing random code reviews with an eye for design system adherence. The design system team had been tracking who used their system the most and invited those engineers to help them review PRs, with the hope that education would be the key to getting more product engineers to use the system. I wanted to create something like that here at Honeycomb for our design system, Lattice.
Moses Mendoza
This is part 2 of a series on taking agentic applications from prototype to production with AWS AgentCore. Part 1 covers foundational design questions, like cardinality, session lifecycle, state management, and operational constraints. This post goes deeper on one of those topics: how we use the difference between an LLM session and an AgentCore Runtime session to build collaborative multi-agent investigations.
Canvas runs on AWS Bedrock AgentCore. We picked AgentCore because it supports the ramp described in this post: it’s quick to get running, it’s modular, and it has production-grade underpinnings. But, like any platform, it has some traps to be aware of. In this post, we share some of the most important things we learned while building on AWS AgentCore.
A first-time engineering manager reflects on the first year leading Honeycomb's SRE team, from a chaotic first week through a year of lessons. Their advice for new managers: invest in relationships, seek feedback relentlessly, and never stop asking questions.
Rox Williams
At O11yCon, we talked to engineering teams across the industry, and the numbers are starting to get genuinely wild: Mixpanel DevOps Engineer Eddie Bracho told us their engineering team is generating 50% more PRs than before AI came into the mix (sorry).
The LLM is rarely the root cause of agent failures. This technical guide shows how to instrument AI agents using OpenTelemetry's GenAI semantic conventions so they appear in Honeycomb's Agent Timeline—including tool calls, multi-agent handoffs, and framework-specific SDKs—so you can debug what actually went wrong.
Fred Hebert
On May 4th in the EU instance, and May 7th in the US instance, Honeycomb ran its only scheduled maintenance session with major planned downtime in the last five years. The maintenance aimed to replace the decade-old Kafka cluster at the core of event ingestion in all of Honeycomb with a newer, more reliable and scalable cluster.
Honeycomb’s Agent Timeline gives you a unified view of LLM behavior and multi-agent workflows, so you can investigate entire conversations and quickly see where prompts, tool calls, and failures happened, in the order they happened.
The second edition of Observability Engineering is available for download on our website.
Old observability metrics like uptime and MTTR aren't enough anymore. Teams must connect technical signals to business outcomes, especially as AI raises the stakes.
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Liz Fong-Jones
This is the fourth installment in the Graviton retrospective series we've been writing since 2021. The methodology is the same one I always reach for: hold the workload constant, run both generations on the same Kubernetes namespace concurrently, and let the per-pod numbers speak.
Jessica Kerr (Jessitron)
The OpenTelemetry Collector is usually deployed as a long-running process, but when telemetry is rare, it makes sense to run it as a Lambda function instead. Here's how to do it.
Austin Parker
Is the era of tokenmaxxing over before it even began? The rise of token leaderboards and the death of token leaderboards at companies like Amazon seem to have taken place in less than three months!
Sara Cave
Our support team leverages our own tools, Canvas and the Honeycomb MCP, to navigate vast and complex internal telemetry when debugging customer issues. This shift means we spend far less time on query mechanics and detective work, and customers get answers faster.
Evaluating Dynatrace alternatives? Compare top observability platforms, including Honeycomb, Datadog, New Relic, Grafana, and more on features, pricing, and complexity to find the best fit for your team.
Ken Rimple
We just wrapped O11yCon 2026, and this year's conversations hit differently. Agent-based software development is here, now. It's no longer an optional choice, and everybody is struggling to understand what their agents are doing and how to make them cost less and perform better. Over the course of both days, we saw clearly that the old assumptions on how and who (or what) writes our software has been upended.
Kale Bogdanovs
Auto-investigations that start the moment a trigger fires. Custom skills that encode your team's runbooks. A live workspace where humans and agents investigate production together. Now available to all Honeycomb customers.
Every LLM call, every tool invocation, every agent handoff, every downstream service span, in one conversation, in one view. Now in Early Access.
