In three years I put an AI agent into production, and then I took it offline with my own hands. It held a good conversation. It answered accurately. And it solved nobody’s problem.
That’s the part almost nobody tells. The market is full of launch stories and empty of shutdown stories — and the second kind teaches more.
This article has two halves. In the first, my honest inventory: what’s running, what’s still in testing, what died, and why the thing that increased my output the most wasn’t artificial intelligence. In the second, the market numbers that explain why this story matters to anyone working in tech today — including why Scrum Master openings fell 62% while 85% of Product Manager openings started asking for AI.
Who’s talking
I started in graphic design. I moved to IT, spent years in WordPress and web development — more than two decades working on sites, servers and things broken in production. Today I work in MarTech: the layer where marketing, systems and money meet.
I’m not an AI researcher. I’m the guy who answers the phone when checkout stops processing on a Friday night. My clients are real companies that, combined, bill more than one million reais per month, and what they buy from me isn’t technology — it’s predictability.
That changes what I call success. A model that impresses in a demo and fails at the edge of the real flow is worth nothing to the people who pay me. That yardstick explains every decision that follows.
Why I stayed quiet
I founded Evolutiva in 2022 and published little after that. It wasn’t a content strategy. It was lack of time and, to be straight about it, some reluctance to talk before I had something to show.
Three years later I do. Including what didn’t work.
The AI agent that went into production and I took offline
It worked. And keeping it live was still a mistake.
A client was getting dozens of calls a day with the same questions: contract status, duplicate invoice, transfer of ownership. The information existed — scattered across thousands of documents, exports and spreadsheets piled up over years.
I built a RAG agent (Retrieval-Augmented Generation — in plain terms: the AI looks the answer up in the client’s real data before responding, instead of making it up). It shipped to production in partnership with another technology company.
The agent understood the question. It found the right document. It phrased the answer clearly. By any conversational metric, it passed.
And I took it offline.
Why I shut down something that worked
Because answering well isn’t the same as resolving.
The client’s system exposed no integration points. The agent could say “your invoice was due on March 12”, but it couldn’t issue the duplicate invoice. It knew a transfer was possible, but it couldn’t start the process.
It was a brilliant employee locked in a room with no phone.
The customer calling in didn’t want information. They wanted the problem solved. The agent delivered half the way — and in customer service, half the way is sometimes worse than nothing, because it creates an expectation and sends the customer back to the queue.
The failure wasn’t the model’s. It was the infrastructure underneath it. And keeping live a system that makes a good impression without delivering results is technical debt dressed up as a showcase.
That was the most expensive lesson I’ve learned, and it became my rule: AI without the ability to act is a demo, not a product.
Why a legacy system became a SaaS before reaching production
Another client ran on a legacy system that could no longer handle the volume: recurring billing, manual reconciliation, spreadsheets passed around by email. I started the rebuild — database migration, containers, data pipeline, automated billing.
Halfway through I noticed something that changed the project: the problem wasn’t that client’s. It was the whole sector’s.
Every business in that market had the same pain, the same manual process, the same spreadsheet. Building a custom solution for one client would solve one case. Building a multi-tenant platform — a SaaS where each company gets its own isolated environment on the same base — would solve the market.
I rebuilt the architecture as SaaS.
Honest caveat: that system hasn’t reached production yet. It’s in pilot. I could write “SaaS platform for the sector” and let you assume dozens of clients are running on it. They aren’t. There’s a finished architecture, a pilot underway and a product decision I stand behind — but production is a word you only use after a real user depends on the system to do their job.
Why the FX desk is still in testing (and will stay there until it’s right)
I built a digital FX trading desk (foreign exchange): quotes, contracts, compliance documentation, all in real time. In it, an AI agent acts as the system’s interface — the operator talks, and the agent executes inside the real flow.
That’s the exact opposite of the first case: here the AI has hands.
And that’s precisely why it’s still in testing.
The system moves real money, from real clients, across different countries. The operation has to be fast, because FX has a quote window. It has to be error-proof, because a wrong digit isn’t a bug — it’s somebody’s loss. And it has to be auditable, because there’s regulation involved.
When the cost of an error is financial and cross-border, “it seems to be working” isn’t a release criterion. Rushing here isn’t agility: it’s recklessness.
What I learned spending more than R$ 25,000 on AI
I’ve invested more than R$ 25,000 in artificial intelligence tools over the past few years — ChatGPT, Gemini, Claude, Perplexity, OpenRouter, OpenCode, in practically every combination of subscription and model that existed in that period.
It wasn’t a course. It was daily use, on work that had to ship.
The most uncomfortable lesson: what I knew changed almost every week. The model that was best for a task in January wasn’t in March. The prompt trick that worked stopped being necessary once the model improved. A tool that looked essential became redundant with one release.
Anyone who treated AI as something you learn once ended up with dated knowledge. What accumulates isn’t the shortcut — it’s the judgment: knowing when a model is good enough, when the problem isn’t the model, and when the right answer is to use no AI at all.
What connects agents to systems: MCP
What ties all of this together today is MCP — Model Context Protocol, a standard adopted by Anthropic, Google and Microsoft to give language models “hands”.
In practice: with MCP, the model doesn’t just talk. It queries databases, triggers processes, records information and returns results inside the system.
Remember the agent stuck in the room with no phone? MCP is the phone. If it had existed with today’s maturity back when I built that project, I probably wouldn’t have shut anything down.
What actually doubled my output — and it wasn’t AI
It was process.
That usually disappoints people expecting the name of a tool, but it’s the honest answer. AI increased my execution speed. Process increased my consistency — and consistency is what clients buy.
I work alone most of the time. Scrum, as written in the manual, assumes a team: a daily with several people, separate roles, an alignment ceremony. Alone, that turns into theater — a meeting with myself.
I adapted it. I kept what solves a real problem for someone working solo:
| Scrum practice | How I adapted it for solo operation |
|---|---|
| Sprint | Closed weekly cycle, with scope frozen at the start |
| Prioritized backlog | Single queue by client impact, not by whatever I feel like that day |
| Daily | Short morning review: what’s blocked and what ships today |
| Definition of Done | Criteria written before starting — without it, “almost done” lasts forever |
| Velocity | Cards closed per week — the only metric I track |
| Retrospective | Half an hour on Friday: what slipped and why |
The gain didn’t come from any single practice. It came from stopping deciding priorities every day. Open scope and a shifting queue burn energy nobody accounts for — and working alone, that energy is the scarcest resource there is.
Result: I’ve been closing roughly twice as many cards per week. Not because I work more hours. Because I stop less, redo less and finish what I start.
The combination that works is this: AI to execute faster, process to execute the right thing. The first half alone produces a lot of disposable work at high speed.
The honest job-market analysis: what rose, what collapsed
While I was building all this, the market reorganized itself in a way almost nobody predicted. I went after the numbers — and some of them are uncomfortable.
What happened to Agile and Product jobs
Hover over the bars to see the source of each number.
Permanent Scrum Master openings in the UK, in 6-month windows. The dedicated role shrank 62% in two years.
What did not fall: the practice. Scrum is still used by 87% of teams. Companies absorbed the method into engineering and cut the separate job title.
The Product Manager market recovered — but the recovery has an owner: AI.
The distance between the third and fourth bars is the market’s open position: 30% of openings ask for AI, fewer than 5% of professionals have actually shipped anything with AI.
Those who have the skill charge more for it — and the premium doubled in one year.
Honest caveat: the job data is mostly from the UK and the US. The direction carries over to Brazil; the exact magnitude does not. Use it as an order of magnitude.
Sources: IT Jobs Watch (Jan 2026) · Dexity, 654 openings (Jul 2026) · PwC (2025) · Lenny’s Newsletter · LinkedIn Workforce Insights · Humanizing Work
Why Scrum Master openings collapsed
Because the practice got absorbed and the separate job title stopped justifying itself.
In January 2023, Capital One cut more than a thousand positions of Scrum Master, agile coach and delivery lead in a single decision. Royal London let go of 90% of its agile coaches a few months later. In the UK, permanent Scrum Master openings fell from 79 to 30 in two years — a 62% drop.
Capital One’s own reasoning is the interesting part: the role was “critical in the early stages of the transformation”, but as the organization matured the natural step was to “integrate agile delivery processes directly into engineering”.
Translated: the method won, the job title lost. Scrum is still running in 87% of teams. What disappeared was the person hired exclusively to facilitate ceremonies.
There’s a second factor, and it’s harder. There are more than 4 million agile certificates issued worldwide. At Scrum.org, fewer than 1% of certificates reached the highest level. When the supply of credentials grows much faster than the supply of proven skill, the market stops being able to tell the two apart — and starts treating everyone as interchangeable.
And the Product Manager? That’s a different story
The product market recovered — it’s the highest volume of openings since 2022, with more than 7,300 positions open globally. But the recovery has an owner.
In an analysis of 654 real Product Manager job descriptions, 85% already mention AI or machine learning and **32% require experience with *evals*** — the tests that measure whether an AI system actually works, instead of just appearing to.
And here’s the number that caught my attention the most: about 30% of open PM roles are AI PM roles, but fewer than 5% of senior PMs have ever put an AI agent into working order.
That gap is the entire opportunity summed up in one line.
What this means in practice
The salary premium for AI skills jumped from 25% to 56% in one year — more than doubling. Demand for those skills grew 142% in twelve months, according to LinkedIn’s own data.
For anyone based in Brazil, the effect is direct: product professionals hired by international companies have been earning in the range of US$ 4,000 to US$ 12,000 per month, typically 2 to 4 times the local equivalent. Payments Engineer roles involving AI pay between US$ 5,000 and US$ 9,500 a month.
A caveat I have to make: these surveys are mostly from the UK and the United States. The direction carries over to Brazil, the exact magnitude doesn’t. Treat it as an order of magnitude, never as a guaranteed salary floor.
How I read all this
It isn’t “Scrum is dead” or “product is over”. It’s something else, and more specific:
The market stopped paying for process in isolation and started paying for process applied to a system that works.
A Scrum Master who only facilitates ceremonies became a cost. A Product Owner who only writes user stories and prioritizes a queue became a cost. What still holds value — and more of it — is whoever has method and understands the system being built.
That’s exactly why the earlier section of this article matters. I don’t use Scrum because I like frameworks. I use it because, without process, AI would just make me produce more garbage in less time. And I don’t sell AI because it’s fashionable. I sell it because I know what breaks underneath it.
The rare combination isn’t knowing AI or knowing process. It’s knowing both and carrying the scars of having done it in production.
Who this article is for
If you run an operation that needs payments infrastructure, automation or AI agents that work in production — and you’re tired of pretty demos that don’t survive the real flow — the conversation is straightforward.
If you build technology teams and you’re looking for someone who has built, broken and shut down their own project and can explain why, same.
I’d rather say “that’s still in testing” than sell certainty I don’t have. In systems that handle money, that’s the difference between a supplier and a problem.
Frequently asked questions
What is an AI agent in production, in practice?
It’s an AI system someone depends on to do their job — not a demo. The difference isn’t in the model, it’s in the ability to act: query real data, execute operations in the system, handle errors and leave an auditable trail. An agent that only talks is a prototype, even if it’s published at a public address.
Why do AI projects fail inside companies?
Most of the time, because of infrastructure, not the model. Disorganized data, legacy systems with no integration points, nonexistent authentication and unmapped processes will take down any agent, however good it is. That’s exactly how my first agent in production failed: the AI was right, the foundation underneath it wasn’t ready.
What is MCP (Model Context Protocol)?
It’s an open protocol that lets language models execute actions in external systems, rather than only generating text. With MCP, an agent queries databases, triggers processes and records information inside the company’s systems. It’s the standard being adopted by Anthropic, Google and Microsoft to connect AI to real tools.
Is Scrum worth using when you work alone?
Yes, as long as it’s adapted — applying the manual without a team turns into empty ceremony. What sustains the gain in a solo operation is the weekly cycle with closed scope, the single queue prioritized by impact and the definition of done set before starting. What you drop are the ceremonies that exist to align several people.
Are Scrum Master jobs disappearing?
Dedicated openings shrank a lot, but the practice didn’t. In the UK, permanent Scrum Master positions fell 62% in two years, and companies like Capital One and Royal London eliminated hundreds of agile roles in one go. At the same time, Scrum is still running in about 87% of teams: organizations absorbed the method into engineering instead of keeping the separate job title. The market stopped paying for facilitation in isolation and started paying for method applied to systems.
Does a Product Manager need to know AI in 2026?
In practice, yes. In an analysis of 654 real Product Manager openings, 85% mention AI or machine learning and 32% require experience with evals. About 30% of open PM roles are specifically AI PM roles, while fewer than 5% of senior PMs have shipped a working AI agent — and that gap is exactly what sustains the 56% salary premium for those who have the skill.
How long does it take to put an AI agent into production?
It depends almost entirely on the state of the systems it will operate, not on the agent. On a base with integrations ready and data organized, weeks. On a legacy system with no integration points, the real work is the foundation — and that usually takes months. Any timeline given before auditing the infrastructure is a guess.
Want to talk about your case? Reach me on WhatsApp or at [email protected].