Honeycomb Blog

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.

The Fundamentals: Fast, Deep, and Ready for What Comes Next - Part 3

The previous posts in this series looked at some of the use cases Honeycomb customers are implementing to observe LLMs in production and power agentic observability workflows. In this final post, we’ll take it back to basics and look at how the fundamental capabilities and infrastructure of Honeycomb provide the comprehensive data and fast performance that makes these use cases work at scale.

AI Working for You: MCP, Canvas, and Agentic Workflows - Part 2

In our previous post, we looked at how Honeycomb provides unique visibility into LLMs operating in your production environment. Now, let’s explore how Honeycomb provides observability insights uniquely suited to helping your AI agents rapidly diagnose and fix production issues.

Honeycomb Is Built for the Agent Era. Here's the Proof - Part 1

AI agents are rewriting how software is built and operated. In this series, you’ll learn about 12 use cases across LLM observability, agent debugging, MCP-powered coding agents, and automated AI investigations that prove Honeycomb is the observability platform built for what comes next.

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.

Create a Custom Service Health Board With the Honeycomb MCP

The best dashboard is one created just for your application, or your service, or your team. You can get that in minutes with the Honeycomb MCP.

The Next Era of Observability: Founders’ Reflections - Additional Q&A

Charity took the time to answer a few additional audience questions we didn’t get to. Dive in below!

Honeycomb 10 Year Manifesto: Observability in a World of AI-Generated Code

When we left Facebook, we were determined to build a tool that would help solve the hardest problems in software. Not by copying everyone else’s architectural hand-me-downs and making incremental improvements, but by borrowing from powerful BI and product tools, and reasoning from first principles about what software engineers need to understand their code in production.

How Time Series Databases Work—and Where They Don’t

In my previous post, we explored why Honeycomb is implemented as a distributed column store. Just as interesting to consider, though, is why Honeycomb is not implemented in other ways. So in this post, we’re going to dive into the topic of time series databases (TSDBs) and why Honeycomb couldn’t be limited to a TSDB implementation.

Why Does Observability Feel so Expensive? (Because it Is)

In Charity's recent webinar with James Governor of RedMonk, she shared that most companies now spend somewhere between 20 and 25% of their entire infrastructure budget on their observability tools.

What Is Observability? Key Components and Best Practices

Software systems are increasingly complex. Applications can no longer simply be understood by examining their source code or relying on traditional monitoring methods. The interplay of distributed architectures, microservices, cloud-native environments, and massive data flows requires an increasingly critical approach: observability.

Observability Day San Francisco: The Future of AI and Observability Is Bright

AI and observability are no longer separate conversations—they’re deeply intertwined. Across keynotes, panels, and demos, speakers at Honeycomb's Observability Day San Francisco unpacked what that means for engineering teams today: faster insights, smarter tools, and new challenges to solve.

How Product Managers Can Benefit From Honeycomb

Observability tools like Honeycomb are built for engineers, not PM teams… but that doesn’t mean there’s no benefit to having your PMs in Honeycomb. Whether it’s debugging a weird customer issue or tracking how a feature is used in the wild, observability gives PMs something traditional product tools can’t: real-time answers with full context, down to a single user.

7 Alternatives to New Relic

New Relic is a well-known application performance monitoring (APM) solution that helps engineers visualize, monitor, and troubleshoot their systems. As businesses evolve, engineers must deliver new capabilities at greater scale and speed, often while managing an increasing number of services and systems. This naturally raises the question: what tools can help?

From Chaos to Clarity: How Honeycomb Tags Are Transforming Developer Workflows

We're thrilled to announce that Honeycomb Tags are now generally available across SLOs, triggers, and boards! Over 100 customers are already actively tagging their observability resources in Honeycomb today.

Honeycomb Named a Visionary in the 2025 Gartner® Magic Quadrant™ for Observability Platforms

Honeycomb Users Are Living in the Future, Part 1: Sampling

Is Your Observability Strategy Boardroom-Ready?

It’s The End Of Observability As We Know It (And I Feel Fine)

In a really broad sense, the history of observability tools over the past couple of decades have been about a pretty simple concept: how do we make terabytes of heterogeneous telemetry data comprehensible to human beings?