For the past half century or more, the teams running enterprise software stacks have nurtured a vast ecosystem of tools to keep software humming along. They’ve built CI/CD pipelines, elaborate test suites, code repositories, and tools galore, all with the goal of keeping the systems up and running.
For the past half century or more, the teams running enterprise software stacks have nurtured a vast ecosystem of tools to keep software humming along. They’ve built CI/CD pipelines, elaborate test suites, code repositories, and tools galore, all with the goal of keeping the systems up and running.
Now AI is throwing a wrench into the whole endeavor. Software is now more complex and sometimes mysteriously unpredictable. Expectations are sky high, but no one is certain what to expect in good conscience.
Luckily, dozens of companies are rushing to build new tools designed to keep agentic stacks on the straight and narrow, delivering value to everyone. The field is rapidly evolving, probably having changed since you started reading this article.
Some of the tools involved in the emerging agent stack aren’t that different from what enterprise teams already have in store. In the end, LLMs are just piles of bits that need to be loaded into a processing chip, just like old-fashioned software. Many of the old tools that watch for overloaded nodes or overpacked disk drives are just as valuable as ever, perhaps more so because LLMs chew through storage even faster. And some of the companies tackling these problems are the same steadfast suppliers who have earned the trust of your DevOps teams; they’ve just extended their own offerings to handle LLMs and AI.
Other challenges, however, are quite different. For example, LLMs are terrifyingly non-deterministic, a real challenge for belts-and-suspenders engineers who traditionally tend the software. Tool vendors are building entirely new systems to handle these somewhat random deviations so that traditional DevOps teams — and their C-suites — don’t have heart attacks.
In context
- Topic: Estrategia y Gobierno de TI — Decisiones de portafolio, costo total, gobierno y marcos de referencia.
- Source: CIO
- Published: 15/10/2026
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Why it matters
There is a wide gap between a company that has technology projects and one that has a technology strategy. The first accumulates initiatives; the second decides what it will not do. Moves like this are usually the moment somebody at the top finally wrote down the list of what gets left out.
I read it looking for coordination cost. Every technology decision splits work across areas that do not report to each other, and that is where the return leaks out. If nobody defined who resolves it when two areas disagree, the project already has its grounding date set.
What usually goes wrong
What usually goes wrong is the part never discussed with the areas that will carry the work. It gets decided at the top, communicated downward, and the resistance shows up not as opposition but as slowness. Nobody says no; everything simply takes twice as long and nobody can explain why.
What to watch
- Whether business metrics appear or only delivery metrics — cost per transaction beats percentage complete.
- The real calendar of contracts and renewals: that is where you see whether the move was strategic or was an expiry date.
- What gets switched off to fund the new thing; if nothing does, the budget will hurt in six months.
How I read this entry
I would treat it less as a technology story and more as a market signal. If one player moves this way, in twelve to eighteen months the rest will have to answer something. Better to decide now, calmly, what that answer will be, rather than improvise it when a client asks.
This entry is an excerpt from the original source, selected by the site radar. The commentary above is the site's own and does not belong to the cited publisher.
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Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
