AI Amplifies Your Existing Practices: Lessons from Our Shift to an AI-First Strategy
As the Honeycomb engineering team worked to double our productivity, we learned a lot. The most important takeaway? Nothing anyone tells you about AI will land if your starting substrate is unhealthy.
Read the full story on Honeycomb BlogHave you worked with this?
The story is what was announced. Nobody has discussed it yet, so if it touches your team, a short post about what you’ve seen helps the next reader.
More from Honeycomb
Recent updates from Honeycomb, so you can tell whether this is a one-off or part of a pattern.
How Adaptive Tail Sampling Works in the OpenTelemetry Collector
A technical deep dive into Honeycomb's adaptive tail sampling processor for the OpenTelemetry Collector: how decisions get made, the samplers available, how thresholds compose with the rest of a sampling pipeline, performance benchmarks, deployment limitations, and how it compares to Refinery.
How Canvas Powers the AI Agent Development Feedback Loop
AI agents need a real feedback loop, not just reactive debugging. This post walks through how Honeycomb's Canvas powers each stage, instrumenting agents with OpenTelemetry, understanding a single run, finding the problems worth fixing, shipping the fix, and proving it worked, plus tips for getting the most out of Canvas.
AI Norms & Values, Part 3 of 3: Things We Hold True
The final part of Honeycomb's AI Norms & Values series: the principles the company holds true about AI as a tool, ownership of work, and rising standards; how it actually uses AI day to day; usage patterns for respecting each other's time; and where it stands on AI's ethical externalities like energy use, IP, bias, and wages.
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What other companies in Observability are doing. The category page shows who’s active, side by side.
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Extend Datadog RUM and Product Analytics to Shopify and Salesforce
Use Datadog RUM and Product Analytics to monitor checkout journeys on Shopify and customer experiences in Salesforce Experience Cloud.
DevRel newsletter: September 2026
Hello from the Elastic DevRel team! In this newsletter, we cover jina-ocr-v1, the latest blogs and videos, and upcoming events like Elastic{ON}.
What if your agent's hallucinations had a budget? How to start using SLOs for agent behavior
At Grafana Labs, observability is what we do. So as we started building AI agents, we naturally reached for the same instincts we bring to every system: measure it, set targets, and make reliability something you can reason about instead of hope for. That instinct led us somewhere unexpectedly useful. It turns out one of the oldest ideas in reliability engineering, the error budget, maps…