There’s a version of learning that feels productive but isn’t.
You’re reading. Watching. Taking courses. Chatting with LLMs. Collecting certificates. Absorbing a genuinely staggering amount of information. And still feeling like something is missing.
I spent most of 2023 and 2024 in that loop. I understood the concepts. I could hold a conversation about agentic workflows, orchestration layers, and human-in-the-loop design. But I hadn’t built anything that worked in the real world. That gap, between understanding AI and building systems that actually function, is the gap nobody talks about. Everyone is teaching you what AI is. Almost nobody is teaching you how to architect around it.
The Side Quest That Wasn’t
At some point I did something that felt completely unrelated. I stepped away from AI entirely and earned my Remote Pilot License. On the surface: a break. A hobby. A side quest.
What I didn’t realize until later is that learning to pilot a drone taught me more about agentic AI governance than most of the courses I’d taken.
A pilot doesn’t manually control every variable. They don’t flap the wings or calculate the lift coefficient in real time. What they do is govern a complex, probabilistic system operating under real-world constraints, with real consequences for failure. You build the pre-flight checklist. You define the operational boundaries. You monitor the telemetry. You know exactly when to intervene and when to let the system run. That’s governance, not execution. And the distinction between those two things turns out to be the most important shift in moving from traditional software to agentic AI.
“I’d been practicing it at altitude. I just didn’t have the vocabulary for it yet.”
The Misconception Running the Market
When I came back to AI, the conversation had shifted from Generative AI to Agentic AI. My first reaction: I’m behind. My second reaction, about two weeks later: actually, no. This is exactly the entry point I was waiting for.
Here’s the misconception I keep seeing everywhere: that AI product success is a function of the model. That the team with access to the most powerful, highest-benchmark model wins. It’s the wrong frame, and I think it’s sending a lot of people in the wrong direction.
The model is one component. It’s not even the hardest component. The true differentiators are the layers underneath:
- Orchestration how multiple models and data sources are coordinated
- Guardrails the operational constraints that keep the system reliable
- Decision points the logic that navigates non-linear, messy real-world workflows
- Human-in-the-loop knowing exactly where to place human oversight, and why
Think of it this way: the model is the engine. The systems around it are everything else, the airframe, the instruments, the checklist, the pilot. An engine sitting on the ground doesn’t go anywhere.
The Shift I Had to Make
Getting from traditional systems thinking to agentic AI required a real shift in how I think, not just what I know. Three things specifically had to change.
People with backgrounds in workflow automation and systems thinking are unusually well-positioned to make this shift. The foundation is already there. The new skills are real, but they’re not starting from zero.
What Building Actually Looks Like
My own understanding crystallized through the Agentic AI Product Management program by Mahesh Yadav. This wasn’t another course to absorb. It was a forcing function to build.
In practice, that meant writing PRDs not as feature lists, but as formal definitions of an agent’s operational boundaries: what it’s allowed to do, what it’s not, and what it must escalate. It meant designing workflows that account for real constraints: API rate limits, latency, data volatility, the messiness of how humans actually respond. It meant thinking through failure modes first, not “will this work?” but “where exactly will this break, and what happens when it does?” And it meant treating cost-per-run as a design input, not an afterthought, because the model you choose for each sub-task should be the cheapest one that meets the accuracy requirement for that task. Running the most powerful model on everything will bankrupt you in production. Defining “good enough” per task is a design decision.
“True clarity doesn’t come from more reading. It comes from designing a system and watching it fail.”
The Expertise You Already Have
If you’ve been in operations, workflow automation, legal tech, or any domain where you managed complex processes with real stakes, you are not behind. You have something most AI-native builders don’t: an intuition for what breaks in the real world.
The failure is rarely in the logic. It’s in the edge cases. The ambiguous inputs. The humans who don’t respond the way the system expected. The third-party API that returns a 200 but sends you garbage. Agentic AI doesn’t eliminate those problems. It inherits them. The people who will build the most resilient agentic systems aren’t the ones who can write the best prompts. They’re the ones who’ve seen enough systems break to know where to put the guardrails before anything goes wrong.
That’s the hidden value of a background in deterministic systems. You already know what it looks like when something silently fails.
Where I Am Now
I’m building. Actively, specifically, imperfectly. GigOps, the agentic booking coordinator I wrote about in the last post, is the first real agentic system I’ve shipped using these principles. Decision logic, human approval gates, a portable architecture that lets me swap components without rewriting everything around them. The program gave me the vocabulary and the framework. The build gave me the proof. Both matter.
If you’ve been in the passive learning loop, consuming, absorbing, waiting to feel ready, here’s the reframe: you’re not behind. You’re overprepared for the wrong thing. The entry point isn’t more information. It’s a system that needs to be governed. And that, you already know how to do.
You’re not behind. You’re overprepared for the wrong thing.
If you’ve been learning but not yet building, what expertise from your past is waiting to be applied to the systems of the future?
This is Latina-in-the-Loop — a running exploration of what it means to build, question, and steward intelligent systems in real time. Follow along.