Operations | Monitoring | ITSM | DevOps | Cloud

InfluxDB 3 Explorer 1.9 makes it easier to work with your existing queries. Whether you’re migrating Flux queries to SQL or you’ve been writing in InfluxQL for years, this release helps bring your existing queries forward instead of starting from scratch. For teams moving to v3 from earlier versions of InfluxDB, query migration is often one of the last major hurdles.

Coding agents like Claude Code, Cursor, and Codex CLI handle the coding parts of building an AI application well. The harder work comes after: understanding why a response went wrong, building eval sets that reflect real production behavior, and keeping up with an application that changes faster than any one-off script can. Teams spend 60–80% of their time on evaluation and error analysis, and much of that work needs to be redone every time the stack shifts.

Developer experience, commonly known as DevEx, describes how an organization’s systems, workflows, tools, and culture affect developer productivity. A positive DevEx leads to tangible organizational benefits, including faster releases, increased innovation, and reduced technical debt. Measuring DevEx enables engineering management to quantify their team’s impact and understand where to direct improvement efforts.

Off-the-shelf models are easy to deploy, but they are rarely enough to solve complex, domain-specific challenges in production. The key to sustained AI value is not in the models themselves but in the ability to tune, evaluate, and refine those models against your organization’s real-time signals. We are excited to announce that Adaptive ML is joining Datadog to accelerate this vision by combining our deep observability data with their expertise in building specialized, high-performance AI agents.

In cloud computing, teams use elasticity and scalability as if they mean the same thing. In reality, the two describe different ways a system handles load, and they solve different problems. Mixing them up can be very expensive. You either pay for capacity that sits idle, or your app buckles the moment traffic spikes, and the bill and the incident report both feel it.

At O11yCon, we talked to engineering teams across the industry, and the numbers are starting to get genuinely wild: Mixpanel DevOps Engineer Eddie Bracho told us their engineering team is generating 50% more PRs than before AI came into the mix (sorry). That kind of velocity is exciting, but it's also a pressure test for every part of your stack that isn't writing code, including your observability practice. Here's what we're hearing from customers about how that's playing out.

Many times, the hardest part of troubleshooting isn’t fixing the actual problem. It’s figuring out where to start. As engineers, it’s easy to lose count of how many times we’ve opened logs, then 10 metrics tabs, and another 10 tabs with trace queries, only to end up back in the logs trying to find a root cause.

When technology works, businesses thrive. Employees stay productive, customers stay connected, and critical services keep running. But when something goes wrong, the real challenge is not only detecting the issue. It is understanding what it affects, who may fell the impact, and how urgently the business needs to respond. That is the value behind the Kyoto release. The latest Skylar One update helps teams better connect IT health to business impact.

AI coding assistants like Claude, Cursor, Codex, GitHub Copilot have become standard tools in the modern engineering workflow. Developers use them to write code, generate tests, and review pull requests. But when something breaks in production, these assistants hit a wall: they have no access to your actual system state. They can reason about logs, traces, and metrics. They just can't see yours.

When something goes down, the last thing you want is to alt-tab between a monitoring dashboard, your on-call tool, and three Slack threads to figure out what is happening and who owns it. That context is usually all there. It is just scattered. The Hyperping MCP server fixes that by putting your monitoring data inside the AI tools you already work in. Your agent can read monitor state, outage timelines, SLAs, and on-call schedules, and answer the questions you would normally chase across tabs.