AI coding agents: how they are changing software development and what your company should control
Artificial intelligence is no longer just collaborating in software development to finalize code or answer queries. AI coding agents can tackle broader tasks, review a repository, plan modifications, edit files, conduct tests, and prepare results for a developer to evaluate.
This evolution continues the path initiated by generative AI in software development but expands the scope from occasional assistance to executing tasks composed of multiple steps.
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When a tool is limited to suggesting code, the developer maintains direct control over almost all actions. When an agent can carry out various stages of a task, part of that work is done with greater independence.
The impact reaches different phases of the software development cycle. Planning, programming, reviewing, and testing can now include work performed by agents. As these capabilities connect with CI/CD and other engineering processes, the need to establish clear boundaries on what each agent can carry out also arises.
For companies, this advancement opens new opportunities for automation but also forces decisions on which tasks can be delegated, which require supervision, and what controls must be maintained before a change moves to production.
What differentiates AI coding agents from code assistants
The first AI-based programming assistants were primarily integrated as support for the developer. They could complete a function, generate a code snippet, explain an error, or propose a solution based on an instruction.
AI coding agents work with a broader unit of work.
Instead of only requesting the generation of a function, a developer can assign a task related to a bug or a new feature. The agent can study the repository, identify related files, make changes, run tests, and prepare a pull request for review.
GitHub documents this type of operation in its coding agents. The system can explore the repository, make changes, and run tests before requesting a person's review. OpenAI describes similar capabilities for Codex, which can work on engineering tasks within isolated environments and execute commands and tests during the process.
The main difference lies in delegating a sequence of actions that previously had to be executed directly by a developer.
This makes the quality of the development environment more important. An agent needs to understand how the repository is organized, what conventions it must respect, how to run tests, and what conditions the result must meet.
The clearer that information is, the better the conditions for the agent to work consistently.
How AI coding agents intervene in the SDLC
The scope of these agents can be analyzed through the Software Development Lifecycle (SDLC). Not all its stages offer the same opportunities for automation nor require the same level of supervision.
During planning, an agent can analyze requirements, existing issues, and components of the repository to identify which parts of the system are related to a task.
In development, it can implement functionalities, fix bugs, or perform refactors. AI then stops producing only isolated snippets and starts working on changes that can affect different files and dependencies.
Code review can also incorporate agents capable of analyzing pull requests, detecting certain issues, and proposing modifications. Testing further expands the cycle. An agent can create tests, run them after modifying the code, and use the results to correct an implementation before presenting it to the developer.
Autonomy can grow as these capabilities connect with integration and delivery processes. Therefore, business analysis must consider both what an agent can do technically and the level of access it needs to do so.
| SDLC phase | What the agent can do | Risk that must be controlled | Recommended control |
|---|---|---|---|
| Planning | Analyze requirements, issues, and repository | Misinterpret the objective | Clear acceptance criteria |
| Development | Create and modify code | Introduce errors or incompatible changes | Review and protect branches |
| Code review | Analyze changes and propose corrections | Omit relevant issues | Review by criticality |
| Testing | Create and execute tests | Validate code with insufficient tests | Testing strategy and quality criteria |
| CI/CD | Interact with automated workflows | Execute unauthorized actions | Permissions and restricted environments |
| Deployment | Participate in delivery processes | Introduce unvalidated changes into production | Approvals and separation of permissions |
Autonomy can be configured gradually. A company can allow an agent to modify code and run tests without granting it the ability to approve or deploy those changes.
AI coding agents can accelerate development without speeding up the entire system
A greater capacity to produce changes does not imply that the entire software process advances at the same speed.
The report DORA 2025 State of AI-assisted Software Development, based on responses from nearly 5,000 tech professionals, found a positive relationship between AI adoption, software throughput, and product performance. At the same time, the study observed that greater AI adoption continued to be negatively associated with the stability of software delivery.
If AI coding agents allow for implementing changes more quickly, the rest of the development cycle must be able to process them at the same pace. Increased productivity during programming can generate more pull requests, more code pending review, and greater demand on testing and integration.
In teams where these processes do not have sufficient capacity, accelerating code generation does not necessarily improve software delivery. The bottleneck may shift to code review, QA, or CI/CD, and even increase the volume of changes that reach these stages without sufficient validation.
For this reason, the impact of AI coding agents must be evaluated across the entire development flow. In addition to completed tasks, it is advisable to observe review times, detected defects, deployment stability, and the team's ability to bring changes to production.
Testing gains importance with AI coding agents
Testing takes on a particularly relevant role when an agent can modify code and then check the results of its own changes.
A reliable testing system provides feedback on the implementation. If a modification causes a failure, the agent can use that result to review its work before presenting it to the developer.
However, running tests does not guarantee that the change is correct by itself.
A suite may have low coverage over critical behaviors, contain unstable tests, or not adequately represent the product requirements. Under those conditions, an agent may pass the available checks without having correctly resolved the issue.
The adoption of coding agents thus increases the value of a well-defined QA strategy. QA engineers can help establish what should be checked automatically, which scenarios need additional validations, and what conditions a change must meet before continuing through the pipeline.
The greater the autonomy of the agent, the more important the quality of the system used to validate its work becomes.
Architecture conditions the autonomy of AI coding agents
Agents work on systems that already have dependencies, conventions, and architectural decisions. The ability to modify code correctly partly depends on whether those relationships can be identified.
A repository with well-defined modules, updated technical documentation, and consistent criteria offers a different context than one where dependencies are hard to follow and a modification can affect seemingly independent components.
Therefore, incorporating agents does not eliminate the need to maintain a comprehensible architecture. It may increase its importance.
The greater the autonomy of the agent, the more necessary it is for the software architecture to establish clear boundaries between components, dependencies, and responsibilities.
This aspect also conditions which tasks are advisable to delegate. A localized correction backed by automated tests offers a different scenario than a cross-cutting modification that affects business logic, persistence, and external integrations.
The company can use these differences to establish different levels of autonomy based on the type of change.
What a company should control when using AI coding agents
The level of access should correspond to the tasks the agent needs to execute.
A system that analyzes a repository requires different permissions than one that can modify code or run tools. The ability to interact with additional services introduces new control needs.
In practice, there are several elements that should be managed explicitly.
- Access to the repository. The agent should only work with the necessary repositories and resources.
- Execution permissions. The available commands and tools must correspond to the work expected from the agent.
- Credential protection. Secrets, tokens, and keys require controls to prevent unnecessary access.
- Change review. The code produced by an agent must integrate into the review processes established by the team.
- Automatic validation. Tests and quality controls must be executed before moving on to later stages.
- Traceability. The team needs to be able to identify the actions taken and the changes made.
- Access to production. The ability to develop a solution does not imply that the agent needs permissions to deploy it.
These controls do not have to be identical for every task. A documentation modification, a localized code update, and a change related to infrastructure do not present the same implications.
Autonomy can be expanded when the environment provides sufficient mechanisms to verify the work done.
AI coding agents also change the work of developers
The automation of more stages of the SDLC modifies part of the work of technical profiles.
Developers still need to understand the product, evaluate implementations, and solve complex problems. However, some execution tasks can shift to agents, while the weight of other capabilities related to defining work and its validation increases.
Backend developers, for example, still need to evaluate business logic, integrations, and the behavior of changes affecting services and APIs, even though part of the implementation can be delegated.
A similar situation occurs with architecture and QA. The automatic generation of changes does not replace decisions about how a system should evolve nor does it determine by itself whether an implementation meets the requirements. Human work partially shifts towards defining objectives, reviewing results, and resolving decisions that require technical or business context.
This also changes the way productivity is understood. Completing a task with less manual intervention can be useful, but the result still needs to fit into a maintainable system and a development process that can validate it.
CI/CD and DevOps determine how far an AI coding agent can go
The connection between agents and CI/CD introduces an important difference compared to isolated code generation.
Modifying files within a development environment has a limited scope. Integrating those changes, executing pipelines, or intervening in delivery processes brings the agent progressively closer to systems with greater operational impact.
DevOps engineers intervene in the permissions, pipelines, and environments that determine how far agents can act within the integration and delivery process.
The separation between development, validation, and production allows for maintaining different levels of control. An agent can have the autonomy to work within an isolated environment and run tests without automatically receiving access to production systems.
This approach allows for expanding delegated tasks without turning technical autonomy into unrestricted access. As AI coding agents take on broader jobs, this separation will be part of the architecture necessary to integrate them sustainably into software teams.
Integrating AI coding agents requires reviewing how software is developed
AI coding agents expand the reach of artificial intelligence within development because they can intervene in tasks that span multiple stages of the SDLC.
Their impact should not be evaluated solely by the amount of code they generate. A company needs to observe how they affect the entire process, from defining a task to its validation and integration.
Testing, architecture, permissions, review, and CI/CD gain greater weight as autonomy increases. If these elements function correctly, agents can take on a larger part of the operational work while maintaining controls over software quality.
The evolution towards development with greater agent participation does not eliminate the role of technical teams. It changes the distribution of work between execution, supervision, and decision-making.
For a company, hiring AI coding agents involves defining what work can be delegated, what conditions the result must meet, and what decisions must remain under human control.
