Context is code. Who believes that is true? There’s two ways that I’ve seen this manifest. One is the like very vibe coding, Andrej Karpathy who says, we’re just giving it prompts. We’re giving it context. It’s writing the code for us. Definitely context is code there. The second piece is something we experienced in our own company. We were writing a piece about onboarding people to our tool. You cannot imagine the discussions we had and then the number of pieces of code we had to write for all the edge cases of doing the onboarding. Then in the end, we said, let’s write a skill in natural language, give it to the agent, it will cater to whatever the person in front of it needs to have, all the scenarios. So much code compressed to a couple of words. That’s also code. It’s not just building it, it’s like a piece of the product as well.

I personally love to think in parallels, and in 2009, I was thinking like, what if ops would be more like dev? We had DevOps. This talk is about what if context is like code. That’s the mindset. The Software Development Life Cycle is the Context Development Life Cycle. I’m not talking about context engineering within your coding agent, but think of it as context. How do we deploy? How do we manage that across our whole setup? We remember the infinity loop, nothing new there. I’ve put a few different words on that. We’re generating context. We’re evaluating whether that context is good. We’re distributing that to team members, the organization to agents, and then we’ll observe whether that actually is working, yes or no, and based on the feedback we get, we improve or generate and come back. Simple loop, same as we try to do with coding. I’m going to walk you through it in the parallels between those four things, and in the end, we’ll have a bonus, the context flywheel. — Read More

Dots are remarkably capable, always-on agents built to handle everything. They’re a whole new way to work with AI—one that gets to know what matters to you, is always working on your behalf, and takes important work off your plate so you get more of your time and attention back. — Read More

#devops

30 CVEs in 60 days, a maintenance tax nobody warned you about, and what engineers are quietly switching to.

… MCP isn’t going away. The downloads are real. The Linux Foundation governance is serious. Multi-vendor adoption means the protocol has institutional staying power.

But the MCP of early tutorials — install a community server, plug it in, done — that version is dead. —  Read More

I love tests. I have always liked tests, and AI has made me think about testing even more. If AI is going to help us write production code, the important question is not whether it can generate a lot of code quickly. The important question is whether we can build a feedback loop that tells us what is actually working.

That was the reason for my talk, “When Tests Lie: Using Observability to Keep AI Honest.” I wanted to understand what happens when you use AI on something much larger than a toy project.

That was part of the experiment. I wanted high quality code. I had architectural opinions. I cared about security, performance, and distributed systems behavior. I also wanted to stay human in the loop, because I wanted to know what went wrong rather than automate everything away from the start. — Read More

Sierra’s Mihai Parparita published field notes on building the company’s internal MCP gateway, and the framing is the most honest thing in it: MCP is “ancient” technology by AI-industry standards, so wiring agents to internal systems should have been straightforward, and instead it became “another engineering iceberg”. The tip above the waterline is the part that demos well: 89% of Sierra employees now connect agents to 45 different services from a single page. The mass underneath is auth.

It’s the best field report on internal agent platforms this year, and every team standing up an agent gateway should read it before writing a line of code. But walk the seven lessons in order and a pattern shows up. The expensive ones are barely about MCP at all. They’re about identity, scopes, consent, and audit. Enterprise software has worked on those four for twenty years, and agents have made all four urgent again at once, in a context where the old answers do not transfer cleanly. — Read More

Tell us the job.We’ll find the right model. Neurometric now has a new site where you can compare the price of any task on various models. — Read More

#devops

It’s a mess, right? If you have not been thoroughly disappointed in AI capabilities, you have not tried enough. And the expectations grow. We’ve been through the ladder of prompt engineering, context engineering, harness engineering. Then Steinberg is tweeting about Loops one month, Graphs the next.

Should your software factory be more autonomous, more dark? Have you sacrificed quality for speed under pressure to deliver? Or is your job now dealing with what happens when other people trade quality for speed? Is AI output mostly just rubbish? Is it all a huge mess? Yes, maybe, maybe not. — Read More

Your AI agent just finished a coding task.

… A few hours later, you notice something strange.

… We keep trying to make AI agents smarter.

But what if the model isn’t the biggest problem?

What if the real problem is the environment we’re putting it in?

That’s where agent harnesses come in. — Read More

We previously noted that, while it’s easier than ever to hit a particular quality bar by having coding agents use effective test techniques, software quality seems to be getting worse, indicating that whatever defaults developers are using may not work very well. Here, we test if simple instructions to agents to use particular techniques or libraries improve implementation correctness, as a kind of test to see how effective agents are when guided by someone with no expertise in testing who’s maybe heard that you should apply certain techniques or use certain libraries. — Read More

#devops

I used to buy books to learn something. I would read, highlight, take notes, and file them somewhere I meant to come back to. Then weeks later, in the middle of a real project, I would try to remember what the book said.

Most of it never made it across that gap. The reading and the applying sat too far apart.

… There is a shorter path now. You can turn a book into a skill your agent loads while you work, so the framework arrives at the moment you need it. Convert it once and the book stops being something you try to remember and becomes something that runs. — Read More