智能體開發生命週期：速度優勢會消退，複雜性仍會留下

Chapter 4 - The chapter where I argue against my own book

AI Coding Makes You Faster. But What Happens to Your Codebase?
AI agents can accelerate development today. The real question is whether your codebase will still be easy to change six months from now.
AI coding tools promise faster development, less repetitive work, and more code delivered in less time.
And they can deliver on that promise.
But there’s a problem we need to take seriously: the speed gains may fade while the complexity remains.
What happens when AI helps a team ship more code, but nobody is responsible for keeping that codebase simple?
That’s the question behind Chapter 4 of The Agentic Development Lifecycle.

🚀 1. Speed Is Easy to Measure. Technical Debt Is Not.
When a team adopts AI coding agents, the immediate benefits are visible:
• More pull requests.
• Faster implementation.
• Less time spent on repetitive tasks.
• More features delivered.
These signals are easy to count.
Technical debt is different.
A change can look correct in isolation while introducing another duplicated function, another dependency, another layer of abstraction, or another architectural inconsistency.
The cost may not appear until someone needs to modify that code months later.
This creates an uncomfortable imbalance:
The benefit is immediate and visible. The cost is delayed and difficult to attribute.
By the time the team notices the slowdown, it may be unclear which decisions caused it.

📉 2. What the Research Suggests
A study of AI-assisted software development by researchers including He, Miller, Agarwal, Kästner, and Vasilescu reported a pattern worth examining:
• Development velocity increased substantially, but the gain was transient.
• Static-analysis warnings and code complexity increased and persisted.
That does not prove every AI-assisted project will accumulate technical debt. Results depend on the team, workflow, repository, and measurement period.
But it raises an important engineering question: Are we measuring how quickly we produce code—or how sustainably we can keep changing it?
Other evidence adds nuance. Some studies have found stable short-term quality indicators or no clear maintainability penalty under particular conditions.
The conclusion should not be that AI-generated code is inherently bad.
The conclusion is that speed alone is not enough to evaluate the health of an AI-assisted development workflow.

🔍 3. The Refactoring Signal
GitClear's analysis of code changes from 2020 to 2024 reported a decline in moved code and an increase in copy/paste changes.

These figures are observational, vendor-published data—not proof that AI adoption alone caused the changes. They should be treated as signals, not universal laws.
Still, they point toward a useful question:
When code production becomes easier, does deliberate code improvement keep pace?
A team can add features continuously while doing less work to simplify the system underneath them.
Over time, that gap matters.

🧠 4. The Hidden Feedback Loop
Before AI agents, developers often acted as an informal maintenance system.
While implementing a feature, a developer might notice:
• Two functions doing almost the same thing.
• A module becoming difficult to understand.
• A dependency spreading across unrelated components.
• A workaround that should become a proper abstraction.
Sometimes they would stop and refactor.
Not because a ticket told them to. Not because a dashboard crossed a threshold. They simply recognized that the code was getting harder to work with.
Now imagine delegating more implementation to agents.
The agent completes the assigned task. The tests pass. The pull request is ready.
But who notices the broader structural problem?
If the workflow rewards task completion without preserving time and ownership for maintenance, that feedback loop can weaken.
The issue is not that AI agents cannot refactor. They can help identify duplication, propose changes, and automate parts of cleanup.
The issue is whether the development process consistently asks them—or the human team—to do it.

⚖️ 5. AI Amplifies the Workflow Around It
An AI coding agent can make a good engineering process faster.
It can also make an incomplete process produce code faster.
If your workflow includes architectural constraints, meaningful reviews, automated tests, and regular maintenance, AI can support those practices.
If your workflow measures success mainly by merged pull requests and completed tickets, those are the behaviors it is likely to reinforce.
This is why the solution cannot be just a better model or a more detailed prompt.
The development lifecycle must include the feedback loops that keep complexity under control.

🛠️ 6. A Practical Experiment for Your Team
You don't need a massive governance program to begin.
Try adding a small set of codebase-health indicators to your regular engineering review.
Track a few signals
• Complexity: Are important modules becoming harder to understand?
• Duplication: Are similar implementations appearing in multiple places?
• Refactoring: Is structural cleanup keeping pace with feature development?
• Delivery stability: Are changes causing more regressions, rollbacks, or rework?
Don't turn these into simplistic targets. Metrics can be gamed, and a rising number does not automatically mean the code is getting worse.
Use them as guardrails that trigger investigation.
Give the signals an owner
For each indicator, decide:

1. What is the baseline?
2. What change deserves investigation?
3. Who is responsible for reviewing it?
4. What action should follow if the trend deteriorates? A metric without an owner is just a chart. Give agents maintenance work, too As an experiment, ask your coding agent to: • Identify one meaningful source of duplication or unnecessary complexity. • Explain why it matters. • Propose a refactor that preserves existing behavior. • Add or update tests. • Show the expected benefits and risks before making broad changes. Keep the scope small. Review the proposal like any other code change. The goal is not to maximize the amount of refactoring. It is to ensure that maintenance remains part of the development process.

🔮 7. The Real Question Is Long-Term Velocity
AI-assisted development creates a tempting trade-off.
The upside is visible: more implementation work can happen in less time.
The downside is less predictable: complexity can accumulate gradually, and the cost may emerge much later.
That doesn't mean adopting AI agents is a mistake. It means evaluating them only by immediate productivity can give an incomplete picture.
The strongest teams will not simply ask:
How much faster can we build with AI?
They will also ask:
How do we keep the codebase understandable, adaptable, and safe to change as AI-generated code accumulates?
That is a lifecycle problem—not just a tooling problem.

💬 Let's Discuss
Who on your team still refactors code without being asked?
And if that person is now delegating more implementation to AI agents, what ensures the codebase still gets simpler when it needs to?
Share your experience in the comments.

📖 Continue reading: Chapter 4 — The Debt Ledger
This post is part of The Agentic Development Lifecycle, a chapter-by-chapter series about building software with AI agents in plain words.

AI #SoftwareEngineering #CodingAgents #TechnicalDebt #DeveloperProductivity