AI coding agents promise to make software development faster. They can write functions, fix bugs, generate tests, explain unfamiliar code, and complete tasks across an entire repository. Forward-deployed agents go even further: they are integrated directly into a company’s systems and workflows, where they can perform practical engineering work with limited human involvement.
For technology companies, this can produce enormous short-term gains. Products may be built faster, smaller teams may accomplish more, and less time may be spent on repetitive programming.
But there is another side to this development.
When AI becomes responsible for an increasing share of engineering work, companies risk creating developers who can produce software without fully understanding it. At the same time, the traditional path into software engineering may begin to disappear. The technology that makes experienced engineers more productive could make it much harder for future engineers to gain experience in the first place.
When Productivity Becomes Dependency
Coding agents can remove a great deal of tedious work. That is valuable, but convenience can gradually become dependency.
An engineer who regularly asks an agent to write implementations, diagnose failures, and make architectural decisions may stop practising those skills personally. The engineer continues to deliver work, so the decline can remain hidden. Pull requests are completed and features are shipped, but the person approving the output may understand less of the system with every release.
This is not necessarily laziness in the ordinary sense. Companies create this behaviour by rewarding speed and visible output. If one engineer carefully investigates a problem while another produces an AI-generated solution in minutes, the slower engineer may appear less productive—even if they develop a much deeper understanding of the system.
Over time, employees learn the lesson: generating results is rewarded more than understanding how those results were produced.
The Illusion of Engineering Ability
AI can make inexperienced developers appear more capable than they are. A person may generate polished code, convincing documentation, and comprehensive-looking tests without being able to explain the underlying design.
This creates an illusion of competence.
The problem becomes visible when the software behaves unexpectedly. Someone must determine which assumptions were wrong, understand the interaction between multiple systems, and decide whether the proposed repair is safe. If engineers have delegated too much of their reasoning to agents, the organization may discover that it has plenty of code producers but very few people capable of diagnosing the system.
AI-generated software can therefore increase both output and fragility. More code is created, while fewer employees understand the complete picture.
Forward-Deployed Agents Could Accelerate the Problem
A standard coding assistant waits for instructions. A forward-deployed agent can be given access to repositories, issue trackers, deployment systems, monitoring tools, and internal documentation. It may investigate problems, modify code, run tests, and prepare changes with relatively little supervision.
This gives the AI company operating the agent considerable influence over how its customer builds software. It also increases the customer’s dependence on the agent.
As these systems become more deeply embedded, companies may begin designing their processes around them. Documentation may be written primarily so agents can consume it. Tasks may be divided according to what agents handle efficiently. Human teams may shrink because management assumes that the AI will provide the missing capacity.
The result could be an organization that appears highly productive but has lost much of its independent engineering knowledge. If the agent makes a serious mistake, becomes unavailable, increases in price, or is withdrawn by its provider, the customer may no longer have enough experienced people to take over.
The Disappearing Junior Engineer
The greatest long-term risk may concern people who have not yet entered the profession.
Junior engineers traditionally learn through relatively small tasks: fixing simple bugs, writing tests, updating internal tools, improving documentation, and implementing straightforward features. These assignments are not always glamorous, but they teach developers how real systems behave.
They also happen to be the tasks that AI agents can automate most easily.
If companies stop hiring junior developers because agents can perform entry-level work more cheaply, the first step of the engineering career ladder disappears. Businesses may continue hiring senior engineers, but senior engineers do not appear automatically. They become senior by spending years making mistakes, reviewing code, responding to incidents, and learning from more experienced colleagues.
Removing junior roles may save money today while producing a serious shortage of experienced engineers later.
What Does It Mean to “Become a Software Engineer”?
For many aspiring developers, learning to code has meant learning a programming language, building projects, and applying for an entry-level position. AI complicates that path.
A beginner can now create an application without understanding every line of code. This lowers the barrier to building useful things, which can be empowering. But it also makes it harder for employers to distinguish between someone who understands software engineering and someone who is skilled only at requesting software from an AI system.
Future engineers may be expected to demonstrate abilities that agents cannot easily replace:
- Breaking unclear problems into precise requirements
- Evaluating whether generated code is correct
- Understanding complex systems and their trade-offs
- Investigating failures when the obvious solutions do not work
- Recognizing security, privacy, and reliability risks
- Communicating with users and other engineers
- Taking responsibility for technical decisions
These are valuable skills, but beginners need opportunities to develop them. A person cannot learn to review complex systems without first working on simpler ones. If companies expect new hires to supervise AI while eliminating the roles in which supervision skills were previously learned, they create an impossible requirement: experience without an opportunity to gain experience.
Companies May Lose More Than Technical Skill
Heavy dependence on AI can also weaken ownership.
An engineer who designs and builds a system usually develops a sense of responsibility for it. They remember why decisions were made, notice when the software behaves strangely, and understand the consequences of changing it.
When an agent generates much of the implementation, employees may begin to see the code as something external—an output to accept rather than a system they created and must maintain. Code review can become superficial, especially when the volume of generated work exceeds the team’s ability to inspect it carefully.
This can lead to a dangerous imbalance: companies produce more software than their employees can responsibly own.
The Power Shifts Toward AI Providers
Forward-deployed agents do not merely change engineering work. They can also transfer power from software companies to AI providers.
The provider may increasingly control the tools through which code is written, analyzed, and maintained. It may gain valuable insight into common engineering problems and organizational workflows. Customers, meanwhile, may become dependent on technology they do not fully control or understand.
This creates difficult questions:
- Who owns the operational knowledge accumulated by the agent?
- Can the customer move that knowledge to another provider?
- What happens if the agent’s behaviour changes after a model update?
- Can the company still operate effectively without the service?
- Who is accountable when AI-generated code causes harm?
- Is the customer developing its own capabilities or renting them indefinitely?
A company may believe it is gaining engineering power when it is actually outsourcing part of its ability to think.
A Better Role for AI
Rejecting AI entirely is neither realistic nor necessarily desirable. Coding agents can reduce repetitive work, improve access to technical knowledge, and help engineers explore unfamiliar systems. The issue is not whether developers should use AI, but what they should continue doing themselves.
Companies should treat human understanding as an essential asset. Engineers should be able to explain, test, challenge, and maintain the systems for which they are responsible. Junior employees should still receive meaningful work, even when an agent could complete it faster. Teams should sometimes solve problems without AI so they can verify that their independent capabilities remain intact.
AI should support engineering judgment, not replace the process through which that judgment is developed.
Conclusion
Forward-deployed agents could make software companies faster, leaner, and more productive. They could also make engineers less curious, organizations more dependent, and entry into the profession far more difficult.
The danger is not simply that AI will replace software engineers. A more subtle risk is that it will remove the experiences through which people become software engineers.
If companies optimize only for immediate output, they may automate away their future experts. They will have more code, fewer people who understand it, and a growing dependence on AI providers to keep everything working.
The real challenge is therefore not teaching AI to produce more software. It is ensuring that, in a world where machines can write code, people still have the opportunity—and the incentive—to learn how software truly works.