Operations | Monitoring | ITSM | DevOps | Cloud

We Know Before it Breaks: Observability-Driven Development

Apr 6, 2026 By Datadog In Datadog
When stakeholders push for faster growth (new markets, new features, newly modernized stack) your engineering model has to change too. At FitnessPassport, the shift from offshore waterfall delivery to an in-house team meant rebuilding not just services, but confidence: legacy systems with weak logging and little visibility made it hard to know whether changes were working and impossible to spot issues before users did. In this talk, Director of Engineering Rob Mitchell will share how FitnessPassport adopted Datadog and used structured logs, metrics, and traces to tighten feedback loops.
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End to End Reliability for all your Workloads

Apr 6, 2026 By Datadog In Datadog
Delivering great products to your customers requires a mix of evolution and consistency. To really land with users your product has to be ready to adapt and scale, prioritizing across a mix of customer and business needs. Join experts in reliability, systems engineering, and DevOps as they share real-world examples, true stories of pitfalls, and astounding impact from the experiments they have run. Learn how experienced practitioners handle failure, adapt to scale, and bridge gaps between teams to improve software performance and customer outcomes.
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pagerduty

How to Prevent and Resolve Incidents Using Model Context Protocol (MCP)

Apr 6, 2026 By Hannah Culver In PagerDuty
The rapid pace of modern software development, fueled by AI-driven coding and accelerated deployment cycles, has resurfaced a challenge that many development teams already struggled with: the speed of incident response must now match the speed of change. Every day, teams ship code faster than ever, which inevitably increases the risk of a new issue making it to production. The traditional approach—where engineers waste time jumping between disconnected tools—is no longer sustainable.
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onpage

From Alerting Tool to Critical Communication Platform

Apr 6, 2026 By Ritika Bramhe In OnPage
Modern operations don’t break down only because alerts are misconfigured or missed. They break down when systems are difficult to manage, slow to adapt, or lack visibility into what’s actually happening in real time. Across industries, teams are managing an increasing volume of critical events. Critical System Alerts. After-hours urgent calls from patients, clients or even emergency lines. Voicemails. Answering service calls, Emergency notifications. Time-sensitive clinical communication.
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harness

AI for GitOps: Tame your Argo Sprawl | Harness Blog

Apr 6, 2026 By Eric Minick In Harness
Innovation is moving faster than ever, but software delivery has become the ultimate chokepoint. While AI coding assistants have flooded our repositories with an unprecedented volume of code, the teams responsible for actually delivering that code, our Platform and DevOps engineers, are often left drowning in manual toil. If you’re managing Argo CD at an enterprise scale, you’re painfully familiar with the "Day 2" reality.
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harness

Ansible vs Terraform Explained: Key Differences for Modern Infrastructure Automation | Harness Blog

Apr 6, 2026 By Mrinalini Sugosh In Harness
If DevOps teams mix up the roles of Ansible and Terraform, deployment pipelines can become unreliable. Manual handoffs slow down changes, and audits may find gaps where responsibilities overlap. Each tool solves different problems, so using them correctly avoids delays and compliance risks. Are you dealing with scattered provisioning and configuration workflows?
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harness

AI Demos Are Easy. Enterprise AI Is Not. | Harness Blog

Apr 6, 2026 By Shubham Jindal In Harness
‍Why 90% of AI prototypes never make it to production, and what to do about it. Every week, someone on my team shows me a demo that looks incredible. An agent that writes deployment pipelines. A chatbot that triages incidents. A copilot that generates test cases from Jira tickets. The demo takes 20 minutes. The audience claps. Everyone leaves convinced we're six weeks from shipping it. We're not.
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honeycomb

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

Apr 6, 2026 By Ken Rimple In Honeycomb
The previous two posts in this series have looked at some of the use cases Honeycomb customers are implementing to observe LLMs in production and power agentic observability workflows. In this third and 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 production scale. AI capabilities built on a weak observability foundation fall apart fast.
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honeycomb

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

Apr 6, 2026 By Ken Rimple In Honeycomb
In our previous post in our series on observability for the agent era, we looked at how Honeycomb provides unique visibility into LLMs operating in your production environment. Now, let’s flip it around and explore how Honeycomb provides observability insights uniquely suited to helping your AI agents rapidly diagnose and fix production issues, and build production feedback into the next round of development.
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