Operations | Monitoring | ITSM | DevOps | Cloud

Every team adopting coding agents hits the same question early: where do you put the instructions that tell the agent how your codebase actually works? Two answers dominate the conversation right now. One is AGENTS.md, a plain markdown file at the root of your repo. The other is skills, packaged instruction sets an agent loads on demand. Most of the debate treats this as a formatting decision. It isn’t.

“We don’t need junior engineers anymore” has become one of those lines people repeat because it sounds obvious. The AI writes the code, so why pay someone to learn how to write it? On the latest Confident Commit podcast, Rob Zuber makes the case that this take is exactly backwards.

I spend most of my day in Claude Code these days. Most of my development processes changed after having access to my own personal assistant. In this post, I'll show you a real-life example of how bug fixes are often done on elmah.io now. I hope it will inspire someone to optimize their workflow and get even more out of their elmah.io subscription.

Here's a scene playing out in engineering teams right now. An engineer spends the weekend running four or five coding agents in parallel. Monday morning, a teammate opens their laptop to 53 changed files with 2000+ diffs and a message that says, more or less, "should be good to merge." Nobody asked for this much output. Nobody has time to review it properly. The team doesn't feel faster. It feels ambushed.

Leadership has stopped asking whether your team is using AI. They’re asking what you’re delivering with it. That’s a harder question, because most of the numbers teams have been reporting, adoption rate, seats activated, prompts run, don’t actually answer it.

AI agent cost in 2026 is mostly a consumption bill, not a subscription. Running an agent costs anywhere from fractions of a cent for a simple routed task to $5 or more for a complex multi-step job, because one request can trigger 3 to 10 model calls behind the scenes. Average production deployments land between $3,200 and $13,000 per month in operational spend. Here is where that money actually goes.

Serverless platforms like AWS Lambda, Google Cloud Run, and Azure Container Apps let teams run applications without managing infrastructure. However, getting full visibility into those workloads has traditionally required a lot of manual setup. A single team may deploy serverless applications across multiple clouds by using tools such as Terraform, AWS SAM, AWS CDK, and the Serverless Framework. Each of these platforms, runtimes, and deployment tools requires its own instrumentation steps.

Managing AI infrastructure in a traditional enterprise data center comes down to validating that sufficient capacity exists before hardware arrives, then maintaining accurate infrastructure data to support planning, deployment, troubleshooting, and ongoing operations. This is because AI has changed what enterprise data centers were built to handle.