I design cybersecurity solutions for the protection of critical infrastructure. Industrial environments, where the cost of a wrong decision isn’t a support ticket. It’s a valve that shouldn’t have opened. The feedback loop between “I asked for this” and “I got this” has consequences that don’t exist in consumer software.
That makes the intent problem visible fast.
The first version of an alert system we built for industrial operators came from clear requirements. Good technical specs, correctly implemented. Operators abandoned it within five days.
The requirements described the system accurately. They didn’t describe the user’s situation at all. What does an operator actually need in the thirty seconds after an anomalous reading? That question wasn’t in the spec. It wasn’t in any of the prompts that built the first version. We knew what to build, not why anyone would use it.
That’s the gap intent engineering closes.
This was always the job
Intent engineering isn’t a new discipline. It’s design’s oldest job wearing new clothes.
Design was never really about pixels. It’s systems thinking, structural clarity, strategic enablement. The force that transforms complexity into coherence, friction into flow, and intent into execution. The designer’s most important contribution was always refinement: of understanding, of relationships, of context, of timing. Designing not just products, but the circumstances that make good outcomes possible.
Anything less than that was always just styling.
For years, that deeper layer was optional. Execution filled the day, and a designer who never articulated intent could still deliver value by producing artifacts. AI removed that cover. When execution is nearly free, the only thing left to contribute is the part that was always the real work: knowing what to build, why, and when.
The designer’s power was never in the immediacy of action. It’s in discerning when to act, when to wait, and when to intervene with the clarity others are missing. The conversation before plan mode is exactly that discernment, applied to a system that will otherwise act immediately, confidently, and without it.
You learn intent by building
Last year I rebuilt my personal website with Claude Code. I didn’t need a new website. I needed to understand what directing an agent actually felt like. Early sessions were mediocre: technically correct responses, completely off. By the end, sessions were sharp. Nothing about the model had changed. What changed was what I brought to it.
That’s the thing you only learn by doing it badly first. Without that experience, you describe what you want in terms of features. With it, you describe what you want in terms of problems. The second framing produces dramatically better outputs, and I’ve watched it happen with colleagues who’ve never written a line of code. The ones who’ve built something ask different questions. They catch wrong turns earlier.
It’s not the code. It’s the model of intent you build while writing it.
Also in June: Intent Engineering for AI Agents · Fifteen Minutes Before Plan Mode · Agents Fail Users at the Decision, Not the Execution
Daniel Arevalo is Product Experience Lead at Omny Security. He builds AI-native tools and writes about design, product leadership, and what it means to direct systems that execute at a speed humans never had before.