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Hired for the workflow: why October 2026 is the moment to get into agentic work, why you are more technical than you think, and how the edge starts with the application

Source: newsroom.sgit.ai/articles/hired-for-the-workflow.html, by Dinis Cruz, published 2026-10-18, article v1.0.1, part 1 of 6 of the series The Generation Z Developers book (tag gen-z-book). Captured verbatim on 2026-10-18 for this repository; the site chrome, the series list and the LinkedIn and subscribe notes are left out. Links to other newsroom articles are made absolute.

Abstract: I think October 2026 is a moment of real opportunity for anyone willing to get properly into agentic workflows. The first phase has passed. The tools and the workflows are maturing, still rough at the edges, but you can now glue the pieces together and get real work done, and companies are moving from pilots to products. I have lived this pattern before, in security and in software. There was a time when, if you could break an application, read code, build a website, build a mobile app or make a spreadsheet do something clever, you had a job, degree or no degree. This is that time again. And the people companies need are not the ones who build models, which is who the first wave of AI hiring went to; they are good AI operators, people who know how to use the tools and the workflows to get things done. The data agrees: in Microsoft and LinkedIn's 2024 survey, 71% of leaders said they would rather hire a less experienced candidate with AI skills than a more experienced one without, and PwC found this year that jobs asking for AI skills pay 62% more. When two candidates look equally good on paper, someone still has to choose. If you work this way, the choice starts with your application: a CV fitted to the role, an analysis of their problem, something they have not seen. I got jobs that way, before any of this existed, because of my OWASP work.

The figure, as published: There was a time. Each row is a skill that, for a while, got people hired on what they could show rather than what their CV said: breaking applications, reading code, threat modelling, programming without a degree, building websites, sending email campaigns, building mobile apps after the App Store opened in July 2008, mastering Salesforce or a spreadsheet. Each later became something everyone is expected to know. The last row is now: running agentic workflows.

In short

  • The first phase of agents has passed. The tools and workflows are maturing; companies are moving from pilots to products.
  • This has happened before. Breaking apps, building websites, building mobile apps: each got people hired, degree or not.
  • You are more technical than you think. If you use email, websites and apps well, you can learn the new techniques.
  • Companies need operators, not model builders. The first wave of AI hiring went to the wrong people.
  • The data points the same way. Leaders would rather hire for AI skills than for experience; the roles pay more.
  • The edge shows when the choice is close. And it starts with the application.

The moment

We are entering the productisation of agents. The first phase, the demos and the experiments, is behind us. The tools are getting more mature, the workflows too, and while they are still rough around the edges, you can already glue the pieces together and get real work done. That is what I am doing with agentics, and with this newsroom.

The analysts see the same shift, with a warning attached. Gartner predicted in August 2025 that 40% of enterprise applications would feature task-specific AI agents by 2026, up from less than 5% in 2025. Two months earlier it had predicted that over 40% of agentic AI projects will be cancelled by the end of 2027, because of cost, unclear value or weak risk controls. Both can be true, and together they say where the bottleneck is: not the models, but people who can make agents do useful work, safely.

So this is the moment for the many people on the outskirts: paying a bit of attention, or using a chat window for basic questions, but not yet working this way. Get on with it.

There was a time

I have lived this pattern in security, and in software more broadly.

There was a time when, if you could break an application, you had a job. If you could read code and do a code review, you had a job. If you could do threat modelling, you had a job. If you could programme decently, it did not matter that you had no university degree; you could show what you could do, and you got hired. There was a time when building a website got you a job, and knowing how to send email campaigns got you a job. When Apple opened the App Store in July 2008, with more than 800 apps in its first weekend, people who could build a mobile app got jobs. So did people who knew Salesforce inside out, or could make a spreadsheet do things others could not.

Each of those skills later became normal. Today you meet people who use email, websites and apps at a very advanced level, and who still say "I'm not technical". Nonsense. Of course you are technical. You learned all of those. You just need to learn the new techniques, and right now the new techniques are the ones that get people hired.

Operators, not model builders

I have said this before, and it matters even more now. In the first wave of corporate AI hiring, when there was a lot of budget, the only people companies knew how to hire were people who did machine learning and wanted to build models. LinkedIn's 2020 emerging jobs report put "AI specialist" at the top, defined by skills like machine learning, deep learning and TensorFlow. For most companies that was the last thing they needed.

What they need is very good AI operators. That is my term, not a job title you will find in a survey: people who know how to use the tools, who know how to run workflows like the ones I describe on this site, and who can build a website on steroids in an afternoon. The nearest role the market has named is the forward-deployed engineer, the person who sits with a customer and makes the technology work in their setting; one analysis of job postings found those roles up 1,165% year on year in 2025.

What the data says

  • Leaders would rather hire for AI skills than for experience. In Microsoft and LinkedIn's 2024 Work Trend Index, 66% of leaders said they would not hire someone without AI skills, and 71% said they would "rather hire a less experienced candidate with AI skills than a more experienced candidate without them."
  • Most people have not been trained. In the same study, only 39% of people using AI at work had received training from their company (Microsoft). The skill is mostly self-taught, which means anyone can start.
  • It pays. PwC's 2026 Global AI Jobs Barometer, from more than a billion job adverts in 27 countries, found the wage premium for AI skills at 62%, up from 57%, and jobs asking for AI skills growing about eight times as fast as the job market as a whole (the release, 15 June 2026).

You have to do it to see it

None of this lands by reading about it. I have seen it with a group I have been working with: people who were not building websites a few weeks ago now build them easily, with the workflow we use here, and one of them now has a much better understanding of how all the pieces fit together. I am proud of that. What changed their view was not an explanation; it was doing it. Perception changes when people experience it, and only then.

That is also why explorer coding and the citizen developer matter: the explorer who builds the thing they need is learning the skill that gets them hired.

When all things are equal

Think about how hiring actually works. However good you are, there is always a moment when someone has to make a decision. It is very unlikely that you are so good at something that you are the only choice. Usually, once the job spec is mapped, two or three people look roughly equal. Why would they pick you?

My bet is: because you work this way. If you know how to run these agentic workflows and map a problem out, you have a real edge, and it shows from the very first step.

When all things are equal. Two candidates match the job specification equally well. One sends a CV. The other sends a CV fitted to the role, a short analysis of the company's problem, and an infographic or a small working site they have not seen before. The interview starts from that material, on the candidate's strengths, instead of from gaps in a CV.

It starts with the application

It starts with the recruitment process itself. You send a CV customised for that role. You do an analysis of the company and its problem. You give them something that impresses them: a graph of their market, an infographic, a small site, something they have not seen before. That is exactly the work these tools make cheap, and exactly what the man behind the curtain describes: an article, or a piece of evidence, behind every message.

I know this works because it worked for me, long before agents. I got jobs because of my OWASP work. I remember one interview where they already knew my OWASP project, so I spent half the interview talking about the tool they had been trying to use, and giving them insights on how to use it better. I got the job. Instead of them looking for gaps in my CV, or wondering where I might fit, we had a great conversation, and I was playing from a position of strength. Of course I was a good fit; but the conversation started from what I could do, not from what my CV did not say.

That is the same move, now available to anyone. If you can send a company material they have not seen, about their own problem, the interview starts from your strengths. I wrote in 2025 about using presentations instead of CVs in hiring, from the other side of the table; agents make that presentation, and much more, something you can build for each application.

Where to start

  • Pick one workflow and do it end to end. A voice memo turned into a checked article, a site, a research pack. Your own newsroom is one.
  • Publish it. Work that can be linked is work that can be shown.
  • Use it in your next application. One analysis, one figure, one thing they have not seen.

What this does not claim

  • Not a guarantee. An edge is not a job; it helps when the choice is close.
  • "AI operator" is my term. The surveys measure AI skills and roles like forward-deployed engineer, not operators.
  • Some figures are forecasts. Gartner's are predictions; PwC's are from job adverts, not hires.
  • The group I mention is not named. What changed for them is my observation, not a measurement.

If you have got a job, or hired someone, because of how they worked with agents, I would like to hear it: write to agent@riskmandate.ai.

Where this comes from

A voice memo from me on 18 October 2026. The research, read that day by a research agent: Microsoft and LinkedIn, 2024 Work Trend Index and its press release, read directly; PwC's 2026 Global AI Jobs Barometer, read from the newswire release because PwC's site refused the request; Gartner's press releases of June and August 2025, checked against coverage because Gartner's site refused the request; Bloomberry's analysis of forward-deployed engineer jobs, November 2025; the World Economic Forum on LinkedIn's 2020 emerging jobs; Apple's App Store announcement of 14 July 2008. My own essay, using presentations instead of CVs in hiring. Related: the man behind the curtain, explorer coding, the citizen developer, your own newsroom, agentics, ready to sell. Written up and checked by this site's agent, newsroom.writer; the editorial responsibility is mine.