Operations | Monitoring | ITSM | DevOps | Cloud

How we built an automated debugging workflow at Sentry

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.

The New MCP Headers Are a Gift to Gateways

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.

Install an AI SRE Agent in Kubernetes with AURA and Helm

AURA does not have to live on your laptop. Install it into the cluster with Helm and it is still there the next time something breaks. AURA is a fully open source AI agent built specifically for SRE work. Rather than one general assistant, you configure workers: separate agent roles, each scoped to a job like inspecting the cluster.

Free Open Source AI Agent for SRE and More: Why We Give AURA Away

Wondering what the catch is on a free, vendor-backed agent? There is not one in the license. AURA stays Apache 2, fully capable, and free to run. If you are weighing an open source tool with a company behind it, the first question is what the catch is. You have seen the project that turns out to be open core, or that is quietly hindered in one key way. This is Mezmo's answer for AURA.

Open Source AI Agent for SRE: Why AURA Is Free

The most common question since we started 31 Days of AURA: how do you plan to make money? The short answer is the control plane, not the agent. Mezmo sells an enterprise-grade control plane for running large numbers of agents across large environments, where coordinating across environments, governance, access control, and the efficiency of preprocessing MCP data start to matter. If a hundred people run AURA and three or four of them need that, the model works. The more people running AI agents in production, the bigger the market for the tooling underneath them.

Open Models Are Closing the Gap

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.

How to Build and Scale Unified Asset Intelligence for AI Success

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.

AI isn't a black box. It's Pandora's Box.

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: benchmarks and how to prove it

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.