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Sep 21, 2026
AI coding agents give Product and Engineering more autonomy, plus a new coordination problem. See how a two-cycle model keeps both moving.
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AI has changed what Product Managers can do.
A PM can now go from an idea to working software in hours. They can build a flow, put it in front of a customer, learn from it, change it, and test again without waiting for every iteration to go through Engineering.
That creates an opportunity for product teams. It also creates a new challenge.
Just because a PM can build something doesn’t mean that thing is ready to become production software.
If we don’t rethink how Product and Engineering work together, faster prototyping can become more code for Engineering to untangle, more unclear ownership, and more pressure to turn experiments into production features.
The answer is to separate exploration from construction.
PMs and engineers are solving different problems
During product discovery, the PM is trying to answer: Should we build this?
That means testing assumptions, changing direction quickly, throwing things away, and getting something real enough in front of a customer to learn from it.
Engineering is solving a different problem: How do we build this correctly?
That means thinking about architecture, security, maintainability, performance, edge cases, and everything else required for software that has to live in production.
Both need AI. But they don’t need the same working conditions.
Exploration benefits from speed, autonomy, and low friction. Construction needs stronger guarantees and guardrails. Trying to optimize the same environment for both creates tension.
So instead of asking how PMs can safely contribute code to the production codebase, there’s a more useful question: What if PMs had their own space to build and validate ideas?
Give PMs a safe place to explore
A PM working with an AI coding agent can build functional versions of new ideas. Not a Figma screen. Not a ticket describing what something might do.
Working software that can be used to test the product experience. The important part is that this environment is separate from production.
That boundary gives the PM freedom to experiment without requiring the same controls we’d expect from production software. The environment can be designed so experiments can’t affect the live product or access things they shouldn’t.
Now the PM’s workflow can look more like:
Idea → build → test with users → learn → iterate
Engineering doesn’t need to be pulled into every cycle. Engineering still matters. It gets brought in once Product has stronger evidence about what’s worth building.
The prototype shouldn’t be the handoff
This is where things can go wrong.
If a PM spends two days building something with AI and then gives the repository to Engineering saying, “It mostly works, can you finish it?”, we haven’t improved the product development process. We may have just moved the mess downstream.
The prototype should help answer product questions. It shouldn’t make technical decisions on Engineering’s behalf.
What Engineering needs from exploration is intent.
- What does the feature need to do?
- How should it behave?
- What did we learn from customers?
- What happens in the important edge cases?
- How will we know when the production version works as intended?
That becomes the handoff.
At Kaizen, we’ve been exploring a model where the bridge between the two cycles is a behavioral specification and a test plan, supported by the working prototype as a reference. The implementation itself stays behind.
What crosses into construction is the spec: a behavioral description, a test plan, and a link to the prototype as reference. The prototype itself stays where it was built.
In simple terms:
Product owns the what. Engineering owns the how.
That distinction matters even more now that AI makes it so easy for both sides to generate code.
What the workflow could look like
A PM starts with a product hypothesis. Instead of immediately turning it into a backlog item, they use AI to build enough of the experience to test it. They put it in front of users. They discover that part of the original idea was wrong. So they change it. They test again.
Once the problem and desired behavior are clear enough, the PM closes the exploration cycle with a clear specification and acceptance criteria.
Engineering then starts from that understanding, not from the PM’s experimental code only. They decide how the feature fits into the architecture, how it should be implemented, what needs to be verified, and how it reaches production.
The result is two parallel forms of autonomy:
- PMs don’t need Engineering for every experiment.
- Engineers don’t inherit implementation decisions from every experiment.
More autonomy doesn’t have to mean less ownership.
AI can make discovery faster
A lot of the conversation around AI in software teams is still about developer productivity. How much faster can we write code? How much implementation can an agent take on?
Those questions matter. But for Product, there may be an even bigger opportunity upstream. AI can shorten the distance between having an idea and learning whether that idea is any good.
That changes what PMs can bring to an engineering team. Instead of: “We think customers want this.” They can increasingly say: “We tested this behavior with customers. Here’s what worked, what didn’t, and exactly what we now need the product to do.”
That’s a much better starting point for building production software.
If you have a prototype and want to take the idea into production, we can help you determine what needs to happen next.
