Coding agents are no longer just editor extensions that suggest a few lines of code. They can break a task into steps, move across files, run tests and even suggest installing a new package when needed. That is a significant improvement to the developer experience. It also adds a new responsibility to the software development process: not assuming that generated code is correct.
I do not think the developers who stand out in 2026 will be the ones who delegate the most work to an agent. The more important question is this: how well do we verify the correctness, security and reversibility of the work an agent performs?
Speed does not mean correctness
When you ask an agent for a new endpoint, migration or React component, the result often looks convincing. The code compiles, some tests pass and the problem appears to be solved at first glance. But working code and correct code are not the same thing.
A generated solution can bypass an existing authorization rule, run an unexpected query or conflict with the application's error-handling approach. These problems are often visible only when the code is considered as part of the system around it. That is why a pull request produced by an AI tool should be reviewed not only line by line, but also through the behavior it introduces to the system.
My rule is simple: code written by an agent should go through the same review process as code written by a teammate. Being generated faster is not a reason to inspect it less.
The agent's workspace is a security boundary
Once a coding agent can access the terminal, file system or network, it is no longer just a chat interface. Installing packages, running scripts and changing files can have real consequences.
That makes it important to keep the agent's permissions aligned with the needs of the project:
- Keep secrets and production access separate from the development environment.
- Do not automatically approve package installations and shell commands.
- Narrow the directories the agent can change and the commands it can run whenever possible.
- Split changes into small, understandable commits.
- Run tests, linting and dependency scans in CI even when the agent says the task is complete.
Dependency installation deserves particular care. When a tool says “install this package”, running the command without checking whether the package exists, whether it is the intended package and whether it is maintained brings a familiar supply-chain risk directly into the development workflow.
Write tests against the agent, not instead of it
When working with AI, the role of tests changes slightly. It is more useful to think of tests not only as a safety net that catches bugs afterwards, but also as a contract that defines the agent's solution space.
For a price calculation function, for example, it is not enough to test the happy path. Fractions, empty values, boundary amounts and multiple currencies should be explicit. For an API endpoint, test more than a 200 OK response: unauthorized access, invalid input, pagination boundaries and repeated requests matter too.
Good tests do not make an agent produce less code. They help it solve the right problem. They are also the most concrete documentation an engineering team has for what must not break during the next change.
Smaller diffs lead to better feedback
When we give an agent a large and ambiguous task, we get a change that is difficult to review. “Improve the checkout flow from end to end” is less useful than “show an error that matches the existing design language when a discount code is invalid”.
Smaller tasks have three advantages:
- It becomes easier to understand why the solution looks the way it does.
- A wrong assumption is discovered earlier.
- Reverting or replacing the change costs less.
The goal is not to slow the agent down. It is to reduce ambiguity. Clear boundaries create a better working environment for both people and agents.
The final decision still belongs to the developer
AI-assisted tools can take over a meaningful part of everyday development. They are genuinely useful for boilerplate, test scaffolding, migrations, documentation and research. But responsibility is not delegated; only part of the work is automated.
For code that is going into production, these questions are still ours to answer:
- What assumption does this change rely on?
- What data does it access and under which permission?
- How does it behave when something unexpected happens?
- Is it easy to roll back?
- Will someone else be able to read this code six months from now?
Using coding agents may become a normal way of working for many teams in 2026. The teams that benefit from that shift will not be the ones that give their tools the most freedom, but the ones that define their trust boundaries most clearly. Speed matters, but in production software, trust is still a prerequisite for speed.