Coding agents gave you engineers. Who is running the engineering team?

Imagine asking a software team to build a diary that helps people connect through shared interests. People can write entries, add photos or record videos. The team proposes matching people whose entries have something in common.

Then a question appears: when two people connect, what can they see?

You might answer “each other's diary.” But seeing an example changes the question. Does connecting today expose everything someone wrote last year? Does using a private entry to find a match also give that match permission to read it? Could two people discover a shared interest without exchanging the writing that revealed it?

These are different products. Choosing among them requires understanding what each would feel like to use. An AI builder can help implement the answer. A useful software team also helps you arrive at an answer you understand.

The work around the code

Using coding agents leaves a founder with several kinds of work: deciding what a request means, recognizing assumptions, understanding tradeoffs, coordinating changes and judging the result. Some of that work belongs with the owner. Some calls for professional judgment from the team.

The boundary matters. You should be able to decide whether people need control over individual diary entries without designing an authorization system. You should be able to ask for a quieter experience without specifying every screen. And you should be able to revise an idea after seeing what it means, without reconstructing the entire project for the next agent.

Tools can already offer planning, specifications, reviews and coordinated execution. In our own experiments with Factory, Kiro and Atoms, all three produced meaningful non-code artifacts. Describing this category as code generation alone would miss much of what they did.

The more useful question is what the process does for the person responsible for the product. Does it help them understand a consequence before approving it? Does the next stage reflect their answer? Can they tell what has actually been delivered?

A document can contain the answer without making the choice understandable

Consider this requirement in an illustrative specification:

After a connection is established, both participants can view the other participant's diary entries.

It is readable. It is also easy to approve without noticing that it includes old entries, or without thinking about an entry someone would share with one connection but not another.

A guided review could instead show two small recipient previews. One exposes the full diary after connecting. The other shows a connection with no entries until the writer chooses to share one. The team can recommend explicit sharing for greater control, show the additional action it requires, and let the owner decide with the consequence in view.

The detailed specification still matters. It gives implementation and verification precise obligations. The owner's understanding should not depend on finding the consequential sentence unaided.

Understanding develops as the product takes shape

A founder may know the outcome they want before knowing every behavior that will support it. Seeing a proposed journey or screen can reveal a preference they could not have articulated at the start.

That makes the timing of questions important. An opening questionnaire cannot do all the work. Nor should a team interrupt for every routine technical choice.

A productive sequence is to propose enough of the experience to make a choice answerable, explain the important assumption, ask for the owner's judgment and continue with that decision attached to the work. Later, a new concrete implication may justify another focused review.

The owner remains responsible for the intended experience. The team earns its role by making those decisions easier to understand and by handling the professional work needed to realize them.

What an accepted decision should change

Suppose the owner chooses explicit sharing per entry. In this illustrative product, that choice has consequences beyond a line in chat.

If the policy later changes, the team also needs to consider existing users and data. Updating the newest screen is only one part of the work.

Our experiments included a supplied change to connection rules. They showed why implementation and explanatory artifacts need separate checks: a working code change can coexist with operating instructions that still describe the old behavior.

What we are building with Orgenix

Orgenix is being developed around this experience: a software team that helps you understand the product as it emerges, make meaningful decisions in context and carry those decisions into subsequent work.

That means using product artifacts during the conversation. A selected journey, proposed behavior or relevant design should help explain the decision in front of you. The intended experience is progressive: explore what matters now, go deeper when useful and let the team exercise judgment within the boundaries you have established.

We are currently optimizing the architecture stage. The product is unfinished. The diary walkthrough above illustrates the intended decision experience; it is not evidence of a completed Orgenix build or a measured reduction in customer effort.

The standard we want to meet is practical. Can an owner explain what is being proposed? Can they recognize an important assumption before it becomes a surprise? Can they revise a choice and understand its effects? Can the team then deliver the result and preserve the decisions that remain in force?

The experiments behind this example are documented in our Factory, Kiro and Atoms field notes.