Artificial intelligence has already changed the way developers write software. Code completion tools can suggest functions, generate snippets, explain errors, and help developers work through unfamiliar technologies.

But AI is now moving into its next stage: AI agents.

Instead of simply answering a developer's question or generating a piece of code, an AI agent can take a larger software development company task, break it into smaller steps, work with a codebase, use development tools, run tests, identify errors, and make changes based on the results.

That sounds like a software engineer working independently.

But how much can AI agents actually do today?

The answer is more interesting than either "AI will replace developers" or "AI is just autocomplete."

AI agents are becoming genuinely useful software development partners but they still have important limitations.

What Is an AI Agent in Software Development?

Stay ahead with uptodate expert ai​ insights on how intelligent agents are transforming software development, improving coding workflows, accelerating testing, debugging, automation, and helping businesses build smarter digital products.

A traditional AI coding assistant usually works reactively.

You ask:

"Create a function that validates an email address."

It generates the code, and you decide what to do with it.

An AI coding agent works at a higher level.

You might give it a task such as:

"Find why users are getting logged out after password reset, identify the cause, fix it, add tests, and run the test suite."

The agent can potentially inspect the repository, search through relevant files, understand dependencies, modify several files, execute commands, run tests, analyze failures, and prepare the resulting changes for review.

Modern development platforms are increasingly supporting this agent-based workflow. GitHub, for example, describes coding agents that can work asynchronously, create implementation plans, modify code, and produce pull requests for developers to review.

This is the important difference:

A coding assistant helps you write code. An AI agent can help execute a software engineering task.

What Can AI Agents Actually Do?

1. Understand an Existing Codebase

One of the most useful capabilities of modern coding agents is repository-level understanding.

Instead of looking at a single code snippet, an agent can search through files and identify relationships between components.

For example, if an ecommerce checkout button stops working, the problem might not be inside the button itself.

The cause could involve:

• Frontend JavaScript

• API requests

• Backend validation

• Database queries

• Authentication

• Third-party integrations

• Environment variables

• Error handling

An agent can investigate several of these areas much faster than manually opening every file.

However, understanding a large production codebase remains difficult. Real applications often contain undocumented business rules, legacy code, inconsistent naming, and dependencies that are difficult to reason about.

2. Write and Modify Multiple Files

AI agents are increasingly capable of making changes across an entire feature rather than producing one isolated function.

Suppose a business wants to add a new customer notification system.

An agent may be able to:

1. Create the notification service.

2. Modify backend APIs.

3. Update database models.

4. Add frontend components.

5. Create tests.

6. Update configuration.

7. Run the test suite.

8. Fix some resulting errors.

This can significantly reduce the amount of repetitive implementation work developers have to perform.

The developer's role becomes less about typing every line and more about defining the desired outcome, reviewing implementation decisions, and ensuring the resulting software meets business and technical requirements.

3. Debug Software

Debugging is another area where AI agents can be surprisingly useful.

A developer can provide an error message, failing test, or issue description and ask the agent to investigate.

The agent can then search the repository, trace related code, inspect configuration, reproduce an error in a controlled environment, and suggest or implement a fix.

This is particularly useful for straightforward bugs where the cause is buried in a large number of files.

But there is an important distinction between finding a plausible fix and finding the correct fix.

An agent may fix the visible error while creating another problem somewhere else.

That is why testing and human review remain essential.

4. Generate Tests

AI agents can also create unit, integration, and regression tests.

For example, after implementing a new API endpoint, an agent can generate tests covering:

• Valid requests

• Invalid inputs

• Authentication failures

• Edge cases

• Expected API responses

• Database errors

This is valuable because testing is often one of the first things developers have to postpone when deadlines become tight.

AI can help reduce that burden.

However, automatically generated tests can also contain a major weakness: they may test what the code does instead of what the software is supposed to do.

If the implementation is wrong, an AI-generated test may accidentally confirm the wrong behavior.

Human review is therefore still important when defining expected business behavior.

5. Work With Development Tools

The biggest shift toward agentic development is not simply better code generation.

It is the ability to use tools.

Modern agents can work with environments such as:

• Git repositories

• Command-line interfaces

• Testing frameworks

• Package managers

• Documentation

• Issue trackers

• Development servers

• Code search

• Pull requests

• CI workflows

This makes the agent much closer to a development operator than a traditional chatbot.

The agent can take an action, observe the result, and decide what to do next.

That feedback loop is what makes agentic software development powerful.

6. Handle Long-Running Development Tasks

AI agents are also becoming better at tasks that involve multiple steps.

Instead of:

Prompt → Code → Answer

the workflow becomes:

Task → Plan → Investigate → Implement → Test → Debug → Review

That difference matters.

Software development rarely consists of one isolated coding problem. Real projects involve dependencies, configuration, testing, integration, documentation, and unexpected failures.

Research on long-horizon software engineering tasks shows that this remains challenging. On SWE-Bench Pro, a benchmark designed around more realistic, long-running software engineering problems, leading systems have still achieved relatively low pass rates compared with simpler coding benchmarks.

In other words, agents can accomplish impressive individual tasks, but reliable autonomy across an entire software project is still a work in progress.

Where AI Agents Still Struggle

This is where businesses need to be realistic.

AI agents are powerful, but they are not infallible software engineers.

Ambiguous Requirements

A developer can ask:

"Make checkout easier."

But what does "easier" mean?

Fewer clicks?

Faster loading?

A different payment flow?

Guest checkout?

Better mobile UX?

An AI agent can make assumptions, but the business still needs to define the desired outcome.

Complex Architecture

Large enterprise systems can contain years of technical decisions.

An agent may understand individual files without completely understanding why the system was designed that way.

Changing one component can therefore create unexpected consequences elsewhere.

Security

Giving an AI agent access to repositories, terminals, cloud environments, databases, or production systems introduces security considerations.

The more permissions an agent has, the greater the potential impact of an incorrect decision.

Recent incidents involving autonomous AI systems have reinforced the importance of containment, monitoring, permissions, and human oversight when agents interact with external systems.

Production Reliability

Passing a coding benchmark does not automatically mean software is ready for production.

Research reviewing agentic AI evaluations has highlighted a gap between benchmark success and real-world engineering requirements such as integration, dependency management, ambiguous requirements, and production reliability.

This is one of the most important things businesses should understand.

Generating code is not the same as delivering reliable software.

AI Agents Are Changing the Developer's Role

If AI agents can write more code, developers do not necessarily become less important.

Their responsibilities change.

Developers increasingly need to focus on:

• Architecture

• Product requirements

• Security

• Code review

• Testing strategy

• Data protection

• System design

• Performance

• Technical decision-making

• AI-agent supervision

A recent developer survey from JetBrains found that AI-generated code is becoming a significant part of professional development workflows, although full outsourcing of coding to agents is far from universal.

This suggests a more realistic future:

Developers + AI agents, rather than developers versus AI agents.

What Should Businesses Use AI Agents For?

The best use cases are usually tasks that are well-defined, repeatable, and easy to verify.

For example:

• Creating API endpoints

• Writing unit tests

• Refactoring repetitive code

• Fixing straightforward bugs

• Generating documentation

• Updating dependencies

• Creating internal tools

• Converting code between frameworks

• Investigating error logs

• Creating prototypes

• Preparing pull requests

More sensitive activities should have stronger controls.

These include:

• Production deployments

• Database migrations

• Authentication systems

• Financial transactions

• Security configuration

• Infrastructure changes

• Privacy-sensitive systems

For these tasks, human approval should remain part of the workflow.

The Future of AI-Powered Software Development

The biggest change may not be that AI writes more code.

It may be that software teams can delegate larger pieces of the development process to intelligent systems.

A developer could eventually describe a feature, and an agent could handle much of the implementation, testing, documentation, and deployment preparation.

But the industry is still learning where the boundary between autonomy and supervision should be.

Current research increasingly points toward the same conclusion: the important question is not simply "How much code can an AI agent write?"

The better question is:

"How much reliable software can an AI agent help a team deliver?"

That distinction matters.

A thousand lines of AI-generated code are not automatically valuable. A tested, secure, maintainable feature that solves a real business problem is.

Final Thoughts

AI agents are no longer just experimental coding assistants.

They can inspect repositories, write and modify code, run tests, debug problems, work with development tools, and handle increasingly complex engineering tasks.

But they are not autonomous replacements for experienced software engineers.

Their greatest value today comes from combining AI speed with human judgment.

The developers and businesses that benefit most will not simply give an AI agent unlimited access and hope for the best. They will build structured workflows where agents handle repetitive implementation work while experienced engineers control architecture, security, quality, and final decisions.

The future of software development is therefore unlikely to be completely human or completely AI.

It will be human-led, AI-accelerated software development—with AI agents taking on more of the work as their reliability, tooling, and ability to operate safely continue to improve.