The Coding Agent No Longer Waits for the Developer: Software Is Being Developed Even When the Team Is Offline

For decades, software development has been directly tied to the presence of the developer.

The developer opens the development environment, analyzes the problem, writes the code, runs the tests, reviews the results, and then decides what the next step should be.

Even when artificial intelligence entered this process, the initial model remained almost the same.

The developer was online.

AI was there to assist.

But coding agents are beginning to change this relationship.

With cloud execution, task delegation, and processes that can continue independently, an agent no longer necessarily needs the developer to remain in front of the screen the entire time.

A task can be delegated.

The agent can continue working in the cloud.

The developer can step away.

And when they return, part of the work may already be complete.

This may look like a productivity improvement.

But in reality, it could be the beginning of a much larger change in the architecture of software development.

From AI That Responds to AI That Continues Working

Most early AI programming tools operated synchronously.

The developer asked a question.

AI responded.

The developer reviewed the response and decided what should happen next.

Even when AI began using tools, terminals, and repositories, the developer usually remained an active part of the session.

The new model is different.

Instead of the developer directing every step, they can define an objective and delegate its execution to an agent.

The agent can analyze the repository, modify files, execute commands, run tests, and continue the process in a cloud environment.

At this point, AI is no longer simply a tool waiting for a command.

It is becoming a process that can continue working after the task has been delegated.

The Cloud Is Giving the Coding Agent a Place to Work

One of the most important changes is the shift of agent execution toward the cloud.

In a traditional model, many of a coding agent’s actions take place on the developer’s local device.

This means the agent depends on the local session, the developer’s environment, and, in many cases, their active presence.

Cloud execution changes this.

The task can be transferred to a remote environment where the agent has the repository, tools, and resources it needs to continue working.

This creates a new separation.

The developer defines the task.

The infrastructure creates the environment.

The agent performs the work.

The result is returned to the team for review.

This makes the development process less dependent on a single human session.

The Developer Can Delegate a Task and Move On to Something Else

This could change the way a developer’s day is organized.

Imagine a developer identifying several tasks in the morning.

One agent takes responsibility for updating a module.

Another analyzes a problem in the tests.

Another process reviews a pull request.

Meanwhile, the developer can focus on architecture, a more complex problem, or a team meeting.

When they return to the delegated tasks, they do not necessarily have to start from zero.

The agents may have produced implementations, test results, or proposals that are already ready for review.

This changes the relationship between human time and software execution time.

In the traditional model, work often stops when the developer stops.

In the agent-based model, the process can continue.

Software Can Be Developed Even Outside the Team’s Active Working Hours

This is where an even more interesting possibility emerges.

If an agent can work in the cloud without a developer constantly monitoring it, then some processes do not have to be limited to the hours when the team is active.

A task can be prepared and delegated.

A process can be executed later.

A check can be performed periodically.

An agent can analyze the results and prepare the work for the next stage.

In the morning, the team may not find only a list of tasks that need to be started.

They may find results that need to be verified.

This does not mean that software should modify itself without oversight.

It means that some parts of the development process can continue without requiring the constant presence of a person.

From Sessions to Tasks

This change may seem small, but architecturally it is highly significant.

Until now, interaction with many AI tools has been organized around sessions.

The developer opens a session.

Provides instructions.

AI responds.

The session ends.

But autonomous agents are shifting this model toward a different concept:

the task.

A task has an objective.

It has an execution environment.

It has tools and constraints.

It has a state.

And it has a result that must be returned to the developer.

This brings AI-driven development closer to other distributed systems in which processes can run independently and results can be retrieved later.

Scheduled Tasks Could Create a New Category of Automation

When coding agents are combined with cloud execution and task scheduling, the possibilities expand even further.

Not every piece of work needs to begin with a direct command from the developer at that exact moment.

Some tasks can be repeated.

For example, an agent can periodically inspect part of a project.

It can analyze new issues.

It can prepare updates.

It can run checks defined by the team.

It can identify cases that require human intervention.

This shifts AI from a tool that is activated only when the developer requests it toward a continuous component of the development process.

Autonomy Does Not Necessarily Mean Independence

An important distinction needs to be made here.

A coding agent that can continue working without the developer being present does not necessarily mean that it should make every decision on its own.

Autonomy can have different levels.

An agent may be allowed to analyze the repository.

It may be allowed to modify an isolated branch.

It may run tests.

It may prepare a pull request.

But it may not be allowed to merge that change into the main branch.

In this way, the process can be autonomous in execution while remaining controlled in decision-making.

This distinction will be essential to the architecture of systems built around coding agents.

The Longer the Agent Works Alone, the More Important Traceability Becomes

When a developer follows every step of an agent in real time, it is relatively easy to understand what is happening.

But what happens when the agent works for an extended period without direct supervision?

The system must then be able to explain the process.

Which files did the agent analyze?

What did it change?

Which commands did it execute?

Which tests passed?

Which tests failed?

What decisions did it make?

At what point did it request, or should it have requested, human intervention?

The more development shifts from interactive sessions to autonomous tasks, the more important this traceability becomes.

The developer should not see only the result.

They should also be able to understand the path the agent followed to reach it.

Autonomous Development Requires Clear Boundaries

Cloud execution creates flexibility, but at the same time it raises an important security question:

What is the agent allowed to do when no one is directly monitoring it?

This is where several of the most important architectural challenges surrounding agents come together.

Can it modify every file?

Can it install packages?

Can it connect to the internet?

Can it use credentials?

Can it create pull requests?

Can it execute workflows?

Can it decide on its own when a task is complete?

Autonomy without boundaries can create risk.

Therefore, autonomous development should not be built only around the capabilities of the model.

It must be built around the boundaries of the system that controls the model.

The Developer Is Moving From Operator to Supervisor

In a traditional model, the developer is the primary operator of the process.

They write the commands.

They modify the files.

They run the tests.

They move from one step to the next.

With coding agents, part of this execution can be delegated.

This does not remove the developer from the process.

But it changes where their value is created.

Instead of manually executing every step, the developer must define:

What objective should the agent achieve?

What resources can it use?

What should it not modify?

Which tests must pass?

In which situations should it stop?

Which results require human approval?

These are increasingly questions of orchestration and architecture rather than manual execution.

A New Working Model May Emerge: The Developer Delegates, AI Executes, the Developer Verifies

If this direction continues, the development cycle may increasingly be organized around three stages.

Delegation.

The developer defines the problem, the boundaries, and the success criteria.

Execution.

The agent works independently in a controlled environment, whether local or cloud-based.

Verification.

The developer or the review system checks the result before the change becomes part of the software.

This model is very different from traditional programming.

The developer is no longer necessarily the person performing every action.

They become the person who designs, controls, and verifies how the work is carried out.

Autonomous Software Development Is Becoming an Architectural Problem

For Soft&Solution Group, autonomous software development should not be viewed simply as a way to produce more code in less time.

It changes the architecture of the development process itself.

The moment a coding agent can continue a task in the cloud even after the developer has left the session, the team must define not only what AI can do, but also how the work it performs without continuous supervision is controlled.

This includes environment isolation, access boundaries, traceability, completion criteria, automated testing, and the points at which human approval is required.

As Ermal Beqiri, founder of Soft&Solution Group, puts it:

“The major step is not simply that AI can write code. The real change begins when a developer can delegate an objective, leave the session, and return to a result that was produced in the meantime. At that point, we are not simply automating coding; we are designing a new system of work in which AI autonomy must be accompanied by boundaries, traceability, and human control.”

Coding agents are gradually changing the relationship between people and the development process.

At first, AI waited for the developer.

Then it began suggesting code.

Then it started using tools and executing commands.

Now the next stage is emerging:

AI that continues working even after the developer is no longer in the session.

This could create software teams that work in a much more asynchronous way.

Humans define objectives and make critical decisions.

Agents carry out an increasingly large share of the execution.

And infrastructure connects these two worlds by defining what can be done automatically and what must wait for a human.

In this model, the question is no longer simply:

“How much code can AI write?”

The more interesting question is becoming:

“How long can the development process continue without the developer being in front of the screen?”