Operations | Monitoring | ITSM | DevOps | Cloud

There is a peculiar moment that happens when talking to AI. You ask it to rewrite an email, it does a good job, and you type, "Thanks!" Then, almost without thinking, you add, "Sorry, one more thing." It is software. It cannot be kept waiting, interrupted, or offended. Still, somehow, you have developed the manners. Then the questions get a little more personal.

September is associated with football, foliage, flannel and, for some, the Financial Plan. As we put pen to paper (or agents to harnesses), there’s a few core elements that have always driven the P&L outlook for the following year: rep productivity and new product releases driving new sales, expansion and contraction against the install base, employee roster changes, and discretionary spend.

Somewhere in your company, a team is three weeks into the AI project that’s going to matter. Somewhere else, a support pilot from the spring is still summarizing every ticket with a frontier model, and nobody has looked at it since it started working. On the invoice they’re identical, and the company has two moves: leave everything open, which funds the waste, or cap everyone, which kills the bet.

So you want your own pet LLM huh? Knowing where to start can be quite tricky, so luckily for you I’ve put together this end-to-end guide. It’ll get you not just started; you’ll end with a fully working chatbot that you’ve fine tuned on the dataset `nampdn-ai/tiny-webtext`, which is a training dataset designed to improve models’ critical thinking abilities. Buckle up, this is going to be both fun and deep.

Ask a simple question "who are our most profitable customers?" and things fall apart. The data lives in six systems, nobody agrees which table is canonical, the column called profit is actually revenue, and the business rules that matter live in someone's head or a Confluence page nobody's touched since 2023. Now point an AI agent at that same mess.

Everything that makes an app or agent real happens after it works on your machine. Locally coding an app is a joy: hot reload, a seeded database, a mocked API key. Then you go to ship it, and "deploy" quietly expands into a Dockerfile that behaves in CI, somewhere to actually run the container, a database it can reach, TLS, secrets wired into the environment, and a pipeline to hold it all together. The feature took an afternoon. The plumbing takes the rest of the week.

Software used to be built by engineers. Not any more. Low-code tools brought in domain teams. AI copilots brought in everyone else. And now agents are building and acting alongside humans; marketing, finance, HR and legal are all shipping the apps they used to file tickets for. The number of people (and things) building on your data has exploded, and it isn't slowing down. And there's no single, standardized way to build, there likely never will be.

AI is rapidly becoming a bigger part of how IT Operations teams detect incidents, interpret signals and decide what to do next.

Jamin Ball’s recent piece, “Systems of Record Won the SaaS Era — Clearinghouses Will Win the Agents Era,” is the cleanest articulation I’ve seen of where the durable moat goes next. His argument is simple and, I think, correct: the SaaS era rewarded whoever owned the system of record, and the agent era will reward whoever owns the clearinghouse.

The hard part of a feature flag is rarely adding it. It is remembering to remove it months later, when the rollout is over, the original context has faded, and there is always a more urgent piece of work waiting. Since April 2026, one Atlassian team has used Agentic Pipelines to clean up their monthly backlog of stale feature flags. The workflow prepares the change and opens a pull request, while engineers still review and merge the pull request.