AI coding tools are changing the development loop.
With tools like Claude Code, Cursor, and GitHub Copilot, a solo developer can build screens, wire APIs, generate logic, and deploy much faster than before.
That is exciting.
But it also creates a new problem.
If we can build faster, we can also move in the wrong direction faster.
That is why I started thinking about Agile again.
Not as a set of meetings.
Not as Scrum rituals.
Not as a way to increase velocity.
But as a way to validate direction.
Speed does not replace direction
Before AI, implementation was often the bottleneck.
Even if a team knew what to build, actually building it took time. That slow process naturally created moments to think, question, and refine the direction.
Now the implementation loop is much faster.
You describe a screen, and code appears.
You describe an API, and endpoints are generated.
You describe a workflow, and an assistant can produce a working draft.
That speed is useful. But it can also hide a dangerous pattern.
You ship one feature, then immediately see the next one.
Then the next.
Then another.
Before asking whether the current feature actually solved a real problem, you are already building the next thing.
Fast completion is not the danger.
Fast certainty is.
Agile was never just about moving faster
Agile is often misunderstood as a fast development methodology.
Run sprints.
Do standups.
Track velocity.
Ship often.
But the deeper point of Agile is not speed.
It is feedback.
Build something small enough to test.
Show it to real users.
Observe what changes.
Adjust direction before the cost of being wrong becomes too high.
A sprint without feedback is just Waterfall with shorter cycles.
This matters even more in the AI era.
AI can accelerate the “build” part of the loop. But it does not automatically answer the more important questions:
- Who is this for?
- What problem does it solve?
- Is this problem painful enough?
- Did the user actually need this?
- What did we learn after shipping?
- Are we improving the product, or just adding more features?
Those questions still belong to people.
For solo developers and founders
This is especially important for solo developers and indie founders.
AI makes it easier to build an MVP in a day.
But an MVP is not just a smaller product.
It is a smaller piece of evidence.
A landing page can be an MVP.
A manual workflow can be an MVP.
Five user interviews can be an MVP.
The goal is not to prove that you can build something.
The goal is to test whether the problem is real.
For founders, Agile is less about team process and more about hypothesis validation:
- Problem hypothesis
- Customer hypothesis
- Channel hypothesis
- Pricing hypothesis
- Retention hypothesis
- Feature hypothesis
If the problem hypothesis is wrong, building more features will not save the product.
For teams using AI
In teams, AI can increase output quickly.
More tickets closed.
More pull requests opened.
More features shipped.
But more output does not always mean more product value.
A team can ship eight AI-assisted features in a sprint and still see no change in retention, activation, or user satisfaction.
That is why the Definition of Done also needs to evolve.
In the AI era, “done” should not only mean:
- code reviewed
- tests passed
- deployed successfully
It should also ask:
- Can the team explain the AI-generated code?
- Does this feature solve a specific user problem?
- Is there a way to observe whether it works?
- Did we define what we expect to learn?
- Will this change inform the next decision?
AI can help us build faster.
But direction still needs to be verified.
Agile as a direction validation loop
The way I now understand Agile is simple:
Question → Hypothesis → Build → Observe → Adjust
AI can accelerate Build.
It may also help with Observe.
But Question, Hypothesis, and Adjust still require human judgment.
That is where Agile still matters.
Not because teams need more rituals.
Not because every company needs Scrum.
Not because velocity should go up.
But because faster development makes wrong direction more expensive.
I wrote a longer Deep Dive on this idea here:
https://dechive.dev/en/deep-dive/ai-era-agile-verification