Operations | Monitoring | ITSM | DevOps | Cloud

Continuous integration and continuous delivery (CI/CD) have dramatically changed how we ship software. But once code reaches production, the operational work is still surprisingly manual. Engineers continually monitor systems, investigate unexpected behavior, and decide which issues require action. And that is where the next opportunity for AI-driven automation lies. For example, in today's CI/CD workflows, someone refreshes the pipeline page to see whether the queue has moved.

For the third consecutive year, Honeycomb has been recognized for its Ability to Execute and Completeness of Vision, and we believe for its strong vision around fast, flexible, high-cardinality querying that helps engineers understand not just that something broke, but why. The software development lifecycle has collapsed. The neat sequence of plan, build, test, and ship that teams have relied on for 20 years is now happening in a single afternoon. AI writes a large share of the code.

This month is a big one! We announced the latest major release of Redgate Flyway Enterprise, built to help teams move fast with AI without losing control of the database. Three key new capabilities also landed in preview: Flyway’s MCP server, advanced support for Databricks, and Oracle schema existence checks. And we're closing with a short, honest read on failure and what it takes to make deployments less stressful. Here's the round-up.

Five coding agents sounds like leverage right up until a developer is the one keeping track of all five: one fixing a bug, one building a feature, one refactoring, and two waiting on input at the same time. AI did not create that problem. It exposed a workflow problem that was always going to surface once parallel development became normal instead of occasional.

Running workloads across AWS, Azure, and GCP is the operational reality for most enterprise engineering teams. The challenge isn't the providers themselves, it's what happens when each one accumulates its own delivery pipeline, its own security configuration, and its own environment management tooling. What starts as provider flexibility quietly becomes provider-specific complexity, multiplied across every team that ships.

Historically, non-technical stakeholders would’ve had most of their data questions answered either through pre-built dashboards or by asking their Data team (or equivalent). Self-serve analytics tools went a step further by offering safe, governed datasets built by Data teams which let non-technical users dig into data without having to worry about how it joins together, how metrics like “revenue” are defined, and so on.

Cursor, Claude, and their fellow tools are great when they are speaking code, but once that code hits production, they are not exactly native. However, if your code is monitored by AppSignal, an MCP server can bridge that gap and allow agents to poke around the production.

Ask most enterprises why their AI program hasn't moved past pilots, and you'll get an answer about the model. It's not accurate enough, not fast enough, not cheap enough yet. Srikanth Akkiraju, who has run transformation at Philips and now at ServiceNow, doesn't buy it. In a recent fireside conversation with iOPEX, he made the case that the model was never the problem. The problem is that most enterprises haven't decided what they actually want AI to change. Five questions came out of that conversation.

AI has quickly become one of the largest and fastest-growing enterprise expenses, exposing many of the same governance and visibility challenges organizations previously faced with cloud. Based on findings from the 2026 State of AI in FinOps report, we explore how mature organizations improve AI cost ownership, reduce waste, and build a culture focused on measurable business value.

When organizations evaluate a software upgrade, the conversation typically centers on risk. Teams consider the maintenance window, the resources required to prepare for the change, the possibility of unexpected issues, and the operational impact of the upgrade itself. These are all legitimate concerns because the people responsible for enterprise platforms are accountable for maintaining service availability while introducing change into complex environments.