For years, AI coding tools mainly worked like advanced autocomplete. You started writing a function, and the tool suggested the next few lines. You accepted the suggestion, edited it, or ignored it.
That is no longer the whole story.
Coding agents can now inspect repositories, search through files, edit code, run commands, execute tests, and work through larger tasks with limited supervision. Instead of helping with one line at a time, they can take on parts of an engineering task from investigation to implementation.
This shift is generally called agentic programming. The idea is straightforward: instead of asking an AI tool to generate a small piece of code, you give it a goal and allow it to take several steps toward completing that goal.
For example, you might ask an agent to add authentication to an application, update the related API routes, write tests, and report anything that still needs attention.
The agent may then inspect the project, identify the relevant files, make a plan, update the code, run the tests, and revise its work when something fails.
This is a meaningful change in how developers use AI. The tools are no longer limited to suggesting the next line. They are increasingly able to take on parts of a development task while developers provide direction, review the work, and remain responsible for the final result.
What Is Agentic Programming?
A normal coding assistant usually responds to a prompt or suggests code where you are currently working. An agent can work across multiple files and use tools while completing a task.
Imagine giving it this request:
Add user authentication, update the API routes, write tests, and list anything that still needs attention.
To do that, the agent might:
- Read the existing project.
- Find the authentication and API code.
- Make a plan.
- Update several files.
- Run the test suite.
- Fix problems found during testing.
- Show you what changed.
The important difference is that the tool is not limited to producing an answer. It can inspect the results of its own actions and continue working.
Most coding agents have a few things in common:
- They can use tools. They may search files, edit code, run commands, or execute tests.
- They work in steps. A task is broken into smaller actions instead of being handled in one response.
- They use feedback. Test failures and error messages can influence the next action.
- They work toward a goal. You describe the result you want rather than every line you expect them to write.
- They use project context. They can look at existing code and follow patterns already used in the project.
That does not make them independent engineers. They still need direction, review, and clear limits.
From Writing Everything to Delegating Parts of the Work
The biggest change is not that AI can write more code. It is that developers can hand over larger pieces of work.
In a traditional workflow, a developer usually handles every stage:
- Understand the requirement.
- Find the relevant code.
- Write the implementation.
- Run tests.
- Fix errors.
- Review the final result.
With an agent, some of the implementation work can be delegated:
- Define the task and its constraints.
- Give the agent access to the required files and tools.
- Let it investigate and make changes.
- Review the work.
- Test the result.
- Decide whether the changes should be kept.
The developer is still responsible for the outcome. The difference is that less time may be spent on repetitive edits and more time on design, review, and technical decisions.
The agent can do some of the work, but the developer still owns the result.
How Agents Work
The language model is only one part of a coding agent. The surrounding system matters just as much.
Planning
Large tasks usually cannot be completed reliably in one step.
An agent may first inspect the project and identify the files involved. It can then create a rough plan and work through it one part at a time.
The plan may change as the agent discovers new information. A file may work differently than expected, a dependency may be missing, or a test may reveal a problem in another part of the project.
Tool Access
An agent needs a way to interact with the development environment.
Depending on the tool, it may be able to:
- Search a repository
- Read files
- Create or edit code
- Run terminal commands
- Install packages
- Execute tests
- Check logs
- Review Git changes
The interface between the model and these tools is important. Clear tool outputs help the agent understand what happened and decide what to do next.
Context and Memory
A task may involve many files and several rounds of changes.
The agent needs to keep track of useful information, such as:
- The project structure
- Files already inspected
- Changes already made
- Test results
- Errors that still need attention
- Requirements from the developer
Too much context can also be a problem. Old or irrelevant information may distract the agent or lead it toward the wrong conclusion.
Testing and Feedback
One useful feature of coding agents is that they can check their own work.
A simple cycle might look like this:
- Make a change.
- Run the tests.
- Read the failure.
- Find the likely cause.
- Update the code.
- Run the tests again.
This can save time, especially when the problem is small and the test output is clear.
Where Agents Can Help
Coding agents are useful for work that is repetitive, well-defined, or time-consuming.
Understanding an Unfamiliar Codebase
An agent can search a large project and answer questions such as:
- Where is this API route defined?
- Which files use this component?
- What happens when a user submits this form?
- Where is this configuration value set?
The answer still needs to be checked, but the initial exploration can be faster.
Repetitive Changes
Agents can help with tasks such as:
- Renaming code across a project
- Updating similar components
- Adding basic tests
- Writing documentation
- Creating simple migrations
- Explaining unfamiliar code
- Making repeated changes across several files
These are often useful starting points, but the generated work should still be reviewed.
Debugging
An agent can read an error, inspect the relevant code, suggest a fix, and run the tests again.
This does not mean it will always find the correct cause. It may fix the visible symptom while missing a deeper problem.
Learning
For newer developers, coding assistants can explain code, compare approaches, and provide examples.
They can be useful as a learning aid, but copying code without understanding it can create problems later.
The Problems Are Real
Giving an agent access to a project also gives it more ways to make mistakes.
Security
An agent with terminal or repository access may be able to run commands, install packages, modify important files, or expose sensitive information.
Possible problems include:
- Adding insecure code
- Using an unsafe dependency
- Changing the wrong files
- Running a destructive command
- Exposing secrets
- Making changes that are difficult to notice during review
Permissions should be limited to what the task requires.
An agent updating documentation does not need access to production systems. An agent changing application code may still need approval before its work is merged.
Confident but Incorrect Code
AI tools can produce code that looks reasonable but is wrong.
An agent may misunderstand the requirement, use an outdated API, or make an assumption that does not match the rest of the project.
A passing test is helpful, but it does not prove that the feature works in every situation.
Technical Debt
An agent may choose the quickest fix instead of the best long-term solution.
For example, it might add another condition to make a test pass when the actual problem is a poorly designed module.
If this happens repeatedly, the codebase can become harder to maintain.
The effect will depend on how the tool is used and how carefully changes are reviewed.
Poor Tool Feedback
Many developer tools were designed for people, not automated systems.
Compiler errors, logs, and debugging output can be difficult for an agent to interpret. Better structured output could make it easier for agents to understand failures and avoid unnecessary changes.
Measuring Real Performance
Small coding benchmarks do not represent everyday software development.
Real projects involve:
- Large codebases
- Unclear requirements
- Multiple services
- Team discussions
- Code reviews
- Changing priorities
- Production issues
An agent that performs well on a short programming problem may still struggle with a large project.
Better evaluations should test longer tasks, repository-level changes, tool use, and collaboration with developers.
Human Review Still Matters
Human review should not mean approving everything the agent produces without reading it.
A developer should be able to check:
- What files changed
- Why those changes were made
- Which tests were run
- What assumptions were used
- Whether the solution fits the project
- Whether the change creates a security or maintenance problem
The amount of review should depend on the task.
A small documentation update may need only a quick check. Changes involving authentication, payments, infrastructure, or user data should receive much closer attention.
What May Change Next
Several areas are likely to develop as coding agents become more capable.
Specialized Agents
Some agents may focus on particular fields, such as:
- Embedded systems
- Security
- Data engineering
- Scientific computing
- Mobile development
- Formal verification
A specialized tool may understand the rules, tools, and common problems of its field better than a general-purpose agent.
Better Development Tools
Programming tools may become easier for agents to use.
Possible improvements include:
- Structured error messages
- Machine-readable test results
- Clear dependency information
- Safer permission controls
- Better execution logs
- Built-in verification tools
These changes could also improve the experience for human developers.
Shared Work Between Developers and Agents
The most practical setup may be a shared workflow rather than complete automation.
For example:
- A developer defines the task.
- The agent explores the codebase.
- The developer reviews the plan.
- The agent implements part of the work.
- Tests are run.
- The developer reviews and approves the result.
The developer and the agent handle different parts of the same task.
Skills That Will Remain Important
If more implementation work can be delegated, some skills may become more valuable.
- Judgment: Knowing whether a solution is correct and appropriate.
- System design: Understanding how one change affects the rest of an application.
- Architecture: Creating code structures that are clear and maintainable.
- Communication: Writing requirements and constraints clearly.
- Security awareness: Recognizing unsafe changes.
- Domain knowledge: Understanding the real problem the software is meant to solve.
- Code review: Finding mistakes that tests and automated checks may miss.
Writing code is still important. Understanding the system and making good technical decisions are just as important.
Conclusion
Coding tools are moving beyond autocomplete.
They can now inspect projects, edit multiple files, use development tools, run tests, and work through larger tasks. That can reduce repetitive work and speed up parts of the development process.
But more capability also creates more responsibility.
Developers still need to define the problem, review the changes, check the results, and take responsibility for what is shipped.
Agentic programming is not about removing developers from software development. It is about changing how some of the work is divided.
The tools may do more of the typing. Developers still need to decide what should be built, how it should work, and whether the final result is good enough.