Making AI development predictable

A year ago the slow part of shipping software was writing it. You scoped a feature, then spent days typing it into existence. Now I hand an agent a ticket and two hundred working lines come back a minute later.

That should feel like pure progress, and most of the time it does. But it moved the hard part somewhere I wasn’t looking.

When typing is free, the cost shifts to the things nobody automated: deciding what’s worth building, trusting what comes back, and planning around work you can’t predict. An agent will write the wrong thing fast and sound sure of itself the whole way. The failure mode now isn’t that the agent can’t do the work. It’s that it does exactly what a vague ticket said, and the ticket was wrong.

I built Indistack because I was stuck in that gap. The agents were producing plenty of code, and I couldn’t answer the basic questions a team asks. What did this cost? How long did it take? Was it even the right thing to build? The code was flowing and the project was a fog.

A tracker built for people can’t close this gap

Jira, Linear, and the rest assume a person triages the backlog, sizes it in a meeting, and hands down something clear. The human is the bottleneck, and the tool is there to coordinate humans.

Bolt an AI tab onto that and you’ve automated the cheap half. Typing gets faster. The judgment stays where it always was: in your head, one ticket at a time. You get faster output, and the same fog arrives sooner.

So the board itself has to change. If the agent does the building, the board has to cover what the agent is bad at: holding the plan, the standards, and a record of what actually happened.

Decide before you build

In Indistack, analysis is a column, not a meeting. Every task stops there before anyone commits to it. It gets broken into steps, ranked by a Dispatch score from the importance you set so the queue orders itself instead of waiting on a triage call, and pinned to a definition of done, written before the first line of code.

By the time the agent starts, the ambiguity is gone. The expensive thinking already happened up front, where changing your mind is cheap, instead of getting buried in a confident wrong answer that’s painful to undo later.

Honestly, this has done more for quality than any model upgrade.

Estimates you can plan around

Every task records when work started, when it finished, and what it cost in tokens and dollars. The board compares the estimate against what happened and learns the gap: by type of work, how far the guesses drift, how many tokens a minute of work burns.

So the estimates stop being guesses. After a dozen tasks, “about forty minutes” is a number the board has earned, not one I made up. You can build a roadmap on that, and give someone a date without crossing your fingers.

Nothing happens in a black box

Open any task and the history is right there: what it was meant to do, what the agent did, how long it ran, what it cost, and a short retro on what went well or got built wrong. When something’s risky, the agent stops and asks before it acts.

Teams underestimate how much this matters. If you can’t see what the agent did, going fast just means piling up risk faster. A few million lines nobody reviewed will turn into a problem you meet later. That visibility is what separates an agent you’re directing from one you’re only hoping about.

What a team gets

Speed is the obvious one. The agent does the building, you spend your time deciding, and you can run several agents at once, each in its own branch.

But speed was never the real bottleneck. What changes is that the work gets predictable and easy to see into. The backlog orders itself. Estimates are calibrated against your own history, not a vendor’s promise or a gut feeling. Every task carries its cost and the reasoning behind it, so a manager can see where the hours and the money went, and a new developer can read why something was built the way it was.

And it gets better the more you use it. The board writes down what each task taught it and carries that into the next one, so the same mistakes stop coming back.

The point

The board doesn’t make the agent smarter. It makes the work legible: clear before it starts, visible while it runs, honest about the cost when it’s done.

That’s what I wanted while shipping a product with these tools, and couldn’t find anywhere. Not somewhere to park tickets, but the layer between a human deciding and an agent doing. You set the direction and define done. The agent brings the speed. The board keeps it all honest.