Operations | Monitoring | ITSM | DevOps | Cloud

If you've discussed AI with your leadership team recently, chances are someone has asked the question.

An AI coding agent holds many conversations at once. Not only is the user prompting it, the agent also talks to the IDE, showing diffs and asking before it touches a file. At the same time it talks to tools, pulling a failing build or querying a database. Two open protocols standardize those conversations. This guide compares ACP vs MCP in practical terms: what each protocol does and when each applies. ACP (Agent Client Protocol) connects a code editor to an AI coding agent.

Monitoring load is not always steady. A team might add a new batch of checks or run several ad hoc tests during a rollout. When that happens, your Private Location agents need to pick up more work at once. If there aren’t enough agents available during a burst, checks start piling up in the queue, which can delay or disrupt check execution. But solving this by running a high number of agents around the clock has the opposite problem: most of that capacity sits idle until the next busy period.

This is a guest post by Dan Mindru, a Frontend Developer and Designer who is also the co-host of the Morning Maker Show. Dan is currently developing a number of applications including PageUI, Clobbr, and CronTool. It feels like with every release, we are walking a tightrope. We need to keep our app lightweight, stable, and performant, all the while depending on APIs that can shift at any moment (without warning, too!).

Harness AI Security provides a unified control plane for AI discovery, risk visibility, and runtime protection, helping organizations operationalize key requirements of the EU AI Act. Instead of relying on manual audits or fragmented tooling, teams get continuous insight into how AI systems are built, exposed, and used, along with the evidence needed to demonstrate compliance.

This OpenAI API cost calculator (also an AI inference calculator for o3/o4-mini thinking tokens) estimates your monthly OpenAI API pricing bill from three inputs: model, request volume, and average tokens per request. Toggle between standard, batch, and cached pricing and get your number in seconds. It also shows what the same workload costs on Claude and Gemini. For the full per-model rate card, see CloudZero's OpenAI API pricing guide.

Benchmarks keep showing that picking the right LLM is hard. The easy answer is "just use the most powerful one." That works, but it is pricey. A small, cheap, or local model can handle many simple requests just as well as a frontier model, for a fraction of the cost. That is what semantic routing is for. Use middleware that looks at an incoming request and decides which model should answer it.