Observability

AMA Recap: More Answers From the Observability Engineering Authors
We couldn't get through every question during our live AMA with the authors of Observability Engineering, so Charity, Liz, George, and Austin stuck around to answer more on AI, telemetry, and what still needs a human in the loop.

Honeycomb Named a Visionary in the 2026 Gartner® Magic Quadrant™ for Observability Platforms
For the third consecutive year, Honeycomb has been named a Visionary in the Gartner® Magic Quadrant™ for Observability Platforms. The recognition reflects Honeycomb's vision for fast, flexible, high-cardinality querying, agent-era observability with Agent Timeline and Canvas, and predictable event-based pricing at trillions of events.

What Comes After Observability?
A year ago, I predicted ways in which AI was about to fundamentally change observability as we knew it. Here's what we've seen happen since—both at Honeycomb and with our customers—and what we're building for the future.

Shipping Is Your Company's Heartbeat: A Letter from a CTO
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.

Lattice Watch: Smarter Guardrails for Design System Observability
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.

15 Best AI Observability Tools for Production Teams in 2026
Compare the best AI observability tools for tracing, evals, token cost tracking, agent workflows, and production reliability in 2026.

Observability vs. Monitoring for AI Systems
Monitoring catches the failures you predicted. AI systems fail in ways you didn't. This post breaks down why AI workloads demand observability—request-level context, distributed tracing, and learning from production—rather than another wall of dashboards.

The Second Edition of Observability Engineering Is Here
The second edition of Observability Engineering is available for download on our website.

Observability: Are You Measuring What Actually Matters?
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.

Top 7 Dynatrace Alternatives in 2026
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.

Top 7 Grafana Alternatives in 2026
Exploring Grafana alternatives? Compare top observability platforms, including Honeycomb, Datadog, Dynatrace, and more, on features, pricing, and ease of use to find the right fit for your team.

Your Questions About AI Agents and Production Feedback Answered
We got a ton of great questions from attendees, and I didn't have time to answer all of them during the session. So, here are my answers to the ones I found most interesting, and most representative of what people are actually grappling with right now.

Scary Things Happen in Production. Context Helps You Find Them.
Your data doesn’t become linearly more powerful as you add more context, it becomes exponentially, combinatorially more powerful with each added attribute.

Evaluating Observability Tools for the AI Era
This guide gives you a more rigorous framework for evaluating observability tools in an era where your AI assistant depends on them as much as your engineers do. The criteria that matter most are not the ones that show up first in a sales cycle.