Operations | Monitoring | ITSM | DevOps | Cloud

Let me start with something most Kafka contributors think but rarely say out loud: nobody understands all of Kafka. I'm not a core committer and have only contributed a few times, but those contributions I have made have been in part thanks to using coding assistants. There are some issues with this approach though, the Apache Kafka project is huge. It's split into many parts: the core, the server, the client libraries, the streams engine, the storage layer, the consensus code, and more.

At Harness, we build an AI-powered software delivery platform, and test result data is core to how we help engineering teams ship faster. The table that stores it started small: one row per record, all the context right there on the row. Simple, readable, and it worked. Until it didn't. This is the story of how we refactored it, what we learned, and what I'd tell you to watch for in your own systems.

Here's the uncomfortable truth about the Mythos era: knowing about a vulnerability and being able to neutralize it are two entirely different problems. AI models like Mythos are finding vulnerabilities 10x faster than humans ever could. Project Glasswing participants discovered over 10,000 high and critical vulnerabilities in their applications. Firefox alone had 271 previously unknown zero-days exposed by Mythos. That's the good news.

AI is going to generate a lot of code from here on out, and a lot of bugs along with it. You already know this. We’ve talked about it before. The bigger challenge is making sure you don’t spend all your time fixing the broken code your agents write. You’re going to need a system that makes it easier to fix those issues for you and fortunately, there are a lot of solutions out there for building automated workflows.

In short, buried in the transport section of the MCP 2026-07-28 release candidate are three changes that matter more to infrastructure teams than to anyone else: mandatory Mcp-Method and Mcp-Name headers, cache-control-style ttlMs and cacheScope fields, and standardized W3C Trace Context propagation. Together with the stateless core, they turn MCP from a protocol that gateways had to fight into one that meets them halfway.

The frontier models have led the pack for a while now. It seems like the big players of Anthropic and OpenAI keep leapfrogging each other by a couple points in benchmark scores every other month. But, a trend we are starting to see is that open weight models are improving by leaps and bounds. They don’t hold the lead and probably won’t for a while, but the fact that open models are scaring the leaders is something to think about.

Most teams adopting AI agents are making a bet on which one wins. Claude or Codex, Copilot or something newer next quarter. That bet is the wrong one to make. The agent you use will keep changing. The workflow around it is what actually needs to hold up.

Every IT leader has felt this tension: your organization has invested in AI, automation and digital operations, and yet outcomes still fall short of expectations. Even with the right tools and intent, you won’t be able to fully realize the value of your AI investments if they’re built on an unsteady foundation.

When CFOs talk about AI budgets, they tend to describe it the same way: it’s a black box, offering little or no transparency. The bill arrives at the end of the month, it’s bigger than last month, and nobody can really explain why. Meanwhile, engineering keeps asking to raise the token budget. I think that framing undersells what’s actually happening out there. If the black box is the bill, the Pandora’s box is what you opened when you brought AI into the company.

Generative AI ROI measures the financial return on generative AI investments relative to their total cost. Benchmarks diverge sharply: Google Cloud's 2025 study found 74% of enterprises see ROI within the first year, while MIT's NANDA initiative found 95% of pilots deliver no measurable P&L impact. The difference is not the AI. It is whether the organization can actually measure cost and outcome at the use case level.