I have been using agentic coding tools on real software work, not just demos. They are useful, but not in the way the hype suggests. The biggest change is delegation. AI coding agents make good technical judgment more important, not less.
I have been using agentic coding tools in real development work.
Not just demos. Not just toy apps. Not just asking a chatbot to write a function in isolation.
I mean actual repo work: reading existing files, fixing bugs, checking rendered output, repairing metadata, tracing side effects, running builds, and deciding what should or should not be changed.
That experience has made one thing clear:
AI coding agents are useful, but not in the way the hype usually suggests.
They are not magic senior engineers. They are not automatic product builders. They are not a substitute for technical ownership.
But they are real leverage.
In fact, this article was drafted with the help of an agentic coding workflow. The same tool was used to inspect my repo, repair SEO front matter, run a local Jekyll build, stage files, and create a commit. That is the point. The useful part was not that the tool had opinions. The useful part was that I could delegate bounded work while still deciding what mattered.
The biggest change is not speed. It is delegation.
Most AI coding discussions focus on speed.
How fast can it write a component? How fast can it generate a backend route? How fast can it scaffold a page?
Speed matters, but it is not the deepest change.
The deeper change is delegation.
Agentic coding tools can now take a bounded engineering task, inspect a repo, make a small change, run verification commands, and report what happened. That changes the developer’s role from manually performing every step to directing a technical process.
That sounds subtle, but it matters.
There is a big difference between:
- “Write me some code”
- “Inspect this repo, find why this page has no metadata, repair only the broken front matter, run the build, and tell me exactly what changed”
The second instruction is closer to how real development work happens. It has context. It has constraints. It has verification. It has a definition of done.
That is where coding agents become useful.
Coding agents are good at bounded tasks
In my experience, agentic coding tools are strongest when the task has a clear boundary.
They are good at things like:
- fixing malformed front matter
- tracing where metadata is generated
- checking rendered HTML output
- writing small utilities
- finding repeated patterns across a repo
- generating boilerplate
- adding narrow tests
- running builds and summarizing failures
- making scoped edits to known files
That kind of work is valuable.
It is also not the same as owning a system.
A coding agent can quickly identify that a Jekyll page is missing its opening --- delimiter. It can add the delimiter, rebuild the site, and confirm that the page now has a <title>, meta description, canonical URL, and sitemap entry.
That is useful.
But the agent did not decide whether the page should exist, whether it should be indexed, whether it supports the site’s content strategy, or whether the surrounding pages are competing with each other.
Those are higher-level decisions.
The agent can help execute them. It does not replace the need to make them.
They struggle with judgment, priority, and restraint
The hardest part of software development is rarely typing the code.
It is deciding what should happen.
Agentic coding tools still struggle when the task requires:
- product judgment
- architectural context
- prioritization
- business tradeoffs
- long-term ownership
- deciding when not to change something
- recognizing that a request is under-specified
- protecting system intent
That last one is important.
Good developers do not just change code. They protect the shape of the system.
Sometimes the right move is a tiny fix. Sometimes the right move is no code. Sometimes the right move is to stop and clarify the business rule. Sometimes the right move is to avoid a refactor because the blast radius is not worth it.
AI coding agents can imitate restraint if instructed carefully. But they do not naturally understand the full business context behind the repo. They do not know which tradeoffs matter unless a human provides that frame.
This is why “the AI wrote the code” is not the interesting question.
The better question is:
Who decided what work was worth doing?
The developer’s role shifts upstream
As coding agents improve, the developer’s role shifts.
Less time is spent typing every line by hand. More time is spent directing work, reviewing output, preserving system intent, and deciding what matters.
That means the developer becomes more like a technical editor, architect, reviewer, and operator.
The job becomes:
- define the task clearly
- provide enough context
- constrain the change
- inspect the diff
- run verification
- catch bad assumptions
- decide whether the result is acceptable
This is still software engineering.
It is just a different shape of software engineering.
And in many ways, it raises the bar. If a tool can generate code quickly, then weak judgment creates bad software faster. The bottleneck moves from typing speed to decision quality.
That is why I do not think AI coding agents make senior developers less valuable.
I think they make senior judgment more visible.
What founders should understand
For founders, AI coding agents are both useful and dangerous.
They can reduce execution friction. They can help build prototypes. They can speed up repetitive work. They can make it easier to explore an idea before investing heavily.
But they do not remove the need for technical ownership.
Someone still has to answer questions like:
- Is this architecture appropriate?
- What happens when usage grows?
- What data model are we committing to?
- What parts of this system are fragile?
- What should be built now versus later?
- What is a prototype, and what is production?
- What risks are being hidden by fast generation?
Those questions do not go away because a coding agent can produce files quickly.
If anything, they become more important.
This is the same pattern I have written about in why hiring a software developer still matters, why vibe coding gets expensive, and why AI-generated code often fails security audits.
Generating software is easier now.
Owning software is still hard.
Agentic tools are leverage
My practical view is simple:
Agentic coding tools are leverage.
They help with bounded execution. They reduce friction. They make it easier to inspect, edit, test, and iterate.
But leverage does not eliminate judgment. It amplifies it.
A clear technical leader can use these tools to move faster without losing control.
An unclear technical process can use the same tools to create more code, more confusion, and more cleanup work.
That is the real shift.
AI coding agents are not replacing developers in the simple way people keep predicting.
They are changing what good developers spend their time doing.
And the more powerful the tools become, the more valuable good judgment becomes.
