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By Kale Bogdanovs
Last week, we launched a major update to Canvas, our investigation workspace. The new Canvas has evolved from an AI co-pilot you chat with to a place where your whole team, human and agent, can work the same problem on the same surface. Auto-investigations begin the moment a trigger, SLO, or anomaly fires. Custom skills encode your team's runbooks so every agent investigates with your team's expertise built in.
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By Dan Juengst
Last week, we introduced Agent Timeline, a powerful new observability experience purpose-built for debugging AI agent workflows in production. Agent Timeline uniquely connects AI-layer visibility to full-stack observability by organizing telemetry around an agentic conversation. A conversation contains one or more agent executions, each of which may contain LLM calls, tool invocations, handoffs, retries, human escalations, and downstream system calls.
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By Howard Yoo
Customers regularly come to us looking to solve their observability problem by connecting the dots from frontend to backend. It sounds straightforward in theory, but in practice it's one of the hardest problems in modern application monitoring. The frontend monitoring tools they already have in place tend to be proprietary or narrowly scoped to frontend needs, leaving them without the context-rich backend data that makes real triage possible.
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By Matthew Scott
Honeycomb is proud to share that we have achieved the Amazon Web Services (AWS) Financial Services Competency. This recognition validates our technical expertise and proven customer success in assisting financial services organizations with building, running, and understanding their production systems on AWS. Securing this competency is a direct response to our customers’ feedback in this space: observability in regulated, high-stakes environments requires more than dashboards and alerts.
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By Shabih Syed
AI is reshaping the SDLC in two directions at once. AI-generated code is shipping faster and with less human supervision than ever before, while agents and LLMs are running directly in production, where they behave very differently from traditional software: non-deterministic, with a wider blast radius than any single function or component, with no stack trace to catch when something goes wrong.
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By Shabih Syed
The software development lifecycle is collapsing. The multi-stage pipeline that defined how software got built and shipped for decades is compressing into rapid loops of intent and validation, with agents now part of the teams building and running it. Day 1 of Innovation Week was about what that shift means for how software gets validated, where observability fits, and the problems that have always been hard but are now genuinely urgent.
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By Mike Goldsmith
Your dataset has hundreds of attributes. Some are self-explanatory: http.response.status_code, server.address. Others are not: meta.refinery.reason, dataset.slug, sli.latency_target_ms. If you don't know what an attribute means, you can't write a good query. And if an AI agent doesn't know what it means, it guesses.
TL;DR: Attribute. More information on one event gives us more correlation power. It’s also cheaper. When you want to add some information to your tracing telemetry, you could emit a log, create a span, or add a piece of data to your current span. Adding a piece of data to your current span is the best! Usually.
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By Mike Goldsmith
Do you receive 50 million log lines per day and struggle to see what actually matters? Health checks, heartbeat pings, connection pool messages—they all drown out the errors and anomalies you're trying to find. Most teams deal with this by writing filter rules to drop the noisy patterns. But those rules are manual, per-pattern, and brittle. A new deployment changes a log format and the filter misses it. A new service starts logging a chatty startup sequence nobody thought to exclude.
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By Midge Pickett
Two things happen when engineers first connect the Honeycomb MCP to their AI assistant. The first is the blank page problem. The Honeycomb UI gives you something to react to: a heatmap, a query builder, a trace to click into. An AI assistant gives you a cursor and nothing else. When you don't know where to start, that's a hard place to be. The second shows up right after you get past the first one. You ask a question, you get a confident-sounding answer, and you're not sure whether to trust it.
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By Honeycomb
Honeycomb and Embrace are extending the rigorous, data-driven practice that Honeycomb pioneered for foundational to mobile and web, giving, site reliability, and platform teams a complete, correlated picture of system health. The strategic partnership makes understanding performance and reliability for every user and every screen part of the observability practice, bringing new depth and standardization to how teams measure end user impact.
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By Honeycomb
Honeycomb has shipped a production integration with Amazon Bedrock AgentCore, surfacing agent telemetry directly in Agent Timeline, Honeycomb's trace view for behavior. It's available now and built on.
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By Honeycomb
Honeycomb's Innovation Week: Observability for the Agent Era (May 12-14) For Day 1 of Innovation Week, Honeycomb co-founders Christine Yen and Charity Majors will share what it actually takes to understand and debug systems in the agent era, and what the best engineering teams are doing differently. A 3-Day Virtual Event for Teams Building the Future May 12: Get insights on how the best engineering teams are tackling the challenges of the agentic era.
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By Honeycomb
Watch this video to see the re-imagined Canvas in action, where auto-investigation has already ranked your hypotheses before you open the tab, multiplayer agents build on each other's work in real time, and a custom skill encoding your team's own runbook can reprioritize the entire incident before you've had your morning coffee.
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By Honeycomb
Watch this video to see Agent Timeline in action: one conversation ID, one view, every agent invocation, LLM call, tool call, and downstream trace, so you stop stitching tabs together and start finding the failure in seconds.
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By Honeycomb
Watch a full replay of all sessions and demos on Day 2 of Honeycomb's Innovation Week.
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By Honeycomb
Honeycomb's Innovation Week: Observability for the Agent Era (May 12-14) For Day 2 of Innovation Week, Honeycomb's product and engineering teams will take you inside the new capabilities purpose-built for the agent era. Expect live demos, real scenarios, and a hands-on look at what it means to own observability for the Agentic era, with AI in Honeycomb to observe AI in production. A 3-Day Virtual Event for Teams Building the Future May 12: Get insights on how the best engineering teams are tackling the challenges of the agentic era.
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By Honeycomb
Canvas skills are how your team's runbooks and tribal knowledge become an active part of the investigation instead of a document someone has to remember to open. Pre-built skills cover the most common investigation patterns out of the box. Custom skills let you encode the specific context, thresholds, and decision logic your team has accumulated, so every auto-investigation starts with your best thinking already applied.
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By Honeycomb
Honeycomb is an event-based observability tool, but you can-and should-use metrics alongside your events. Fortunately, Honeycomb can analyze both types of data at the same time. When maturing from metrics-based application monitoring to an observability-based development practice, there are considerations that can make the transformation easier for you and your team.
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By Honeycomb
Evaluating observability tools can be a daunting task when you're unfamiliar with key considerations and possibilities. This guide steps through various capabilities for observability tooling and why they matter.
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By Honeycomb
This document discusses the history, concept, goals, and approaches to achieving observability in today's software industry, with an eye to the future benefits and potential evolution of the software development practice as a whole.
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Honeycomb is a tool for introspecting and interrogating your production systems. We can gather data from any source—from your clients (mobile, IoT, browsers), vendored software, or your own code. Single-node debugging tools miss crucial details in a world where infrastructure is dynamic and ephemeral. Honeycomb is a new type of tool, designed and evolved to meet the real needs of platforms, microservices, serverless apps, and complex systems.
Honeycomb provides full stack observability—designed for high cardinality data and collaborative problem solving, enabling engineers to deeply understand and debug production software together. Founded on the experience of debugging problems at the scale of millions of apps serving tens of millions of users, we empower every engineer to instrument and query the behavior of their system.