I’ve been building software for a long time. And for the last year, like everyone else, I’ve been building with AI agents.
They are astonishing. They scaffold systems in minutes that used to take weeks. They migrate stacks, refactor architectures, design APIs on the fly.
But something kept breaking. Not the code. The meaning.
The pattern
Every project followed the same arc. I’d describe what I wanted. The agent would build something impressive, close, often very close, but subtly wrong. Not wrong in syntax. Wrong in interpretation.
So I’d clarify. Add constraints. Explain edge cases. The agent rebuilt. Better, but different. Assumptions had shifted. Decisions had been reinterpreted. The original idea dissolved into patches.
Here’s a small example. I asked an agent to build a booking system. It did, beautifully. But when two users booked the same slot, it silently let both through. I never said “handle double bookings” so the agent never asked. It made a choice I didn’t know about until a user hit it.
That’s not a bug. That’s an intent gap.
The real problem
Agents build fast. They just don’t build what you meant.
Even when you write a PRD. Even when you maintain AGENTS.md. Even when you carefully document constraints. The intent still lives scattered across prompts, memory, comments, and assumptions. And when intent is scattered, every rewrite becomes reinterpretation.
This wasn’t a tooling problem. It wasn’t a model problem. No better prompt or smarter model would fix it, because the problem happens before the first line of code, in the gap between what you said and what you meant.
The idea
What if the conversation itself could become the specification?
Not a summary written afterward. The actual dialog, fully clarified, every ambiguity resolved, every decision classified as non-negotiable or flexible. A conversation that keeps going until new questions stop producing new answers.
That converged result needed a name. I called it a canon.
A canon is not code. It’s not tied to a framework, doesn’t mention APIs, doesn’t depend on a model. It’s a durable expression of what you actually meant. Agents build from it, verify against it, and rebuild from it when the stack changes. The code is replaceable. The canon stays.
What The Pantion Dialog does
The Pantion Dialog exists to close the intent gap before anything is built. It interviews you, not your documents, until there’s nothing left to guess. It classifies every decision as HARD or FLEX. It stops when further questions no longer change behavior.
The result is a canon that any agent, any model, any tool can build from. Today and five years from now.
This is still early
Pantion is early. But the idea behind it is simple:
Before agents write code, they should understand what you actually mean.
And something I didn’t expect: the same approach works for images and video. Different questions, same principle. Turns out intent gaps aren’t a software problem — they’re a communication problem.
That’s what The Pantion Dialog is for.