How to prevent artificial intelligence from replacing you? Instructions from the Associate Vice President of AI and Data Science at SoftServe

Will developers really be out of work?

A few months ago, Mark Zuckerberg predicted that most code will soon be written by AI — and we’re not just talking about autocompletion, but about the full cycle: setting goals, running tests, fixing bugs, and writing code better than lead engineers.

We’re already seeing this shift happening. Large language models (LLMs) are becoming autonomous agents that plan, analyze, execute processes, and integrate with APIs. They’re starting to behave like junior developers. This shift is both exciting and unsettling. If your daily work involves glue code, dashboards, or data transformation scripts, there’s a good chance an agent can do it faster than you.

At Microsoft, 30% of its code is already written by AI. 91% of companies use AI agents, most often to automate tasks, according to research by Okta. These numbers can be alarming, prompting the question: are developers really going to lose their jobs?

Not exactly. Even the best AI falters on large systems, where there are no clear requirements and there is a changing context. Agents lack the ability to make judgments, they lack domain expertise and they cannot think across multiple layers of architecture. But one thing is clear: the role of the engineer is changing.

In our experience at SoftServe, agent-based AI is changing the development process itself. We have integrated agents to generate backend logic, fully functional UIs, CI/CD configs (including Terraform). They do not build ready-made systems, but create first versions of solutions based on available data, such as PRD. The result is far from ideal — and we do not expect it to be. But it is enough to remove the «clean slate» effect and provide a structure that engineers are already refining. That is, the role of the developer shifts from writing code from scratch to shaping and improving the results of AI agents.

The industry is seeing a growing number of AI-based tools. Cursor helps developers iterate directly in the IDE. Lovable and V0 help build UI components. Windsurf explores more complex composition cases. Each tool provides partial automation, but most of them are closed-source and «black boxes.» You can’t control their behavior or customize them to your stack. That’s why many companies build their own internal agents: inspired by what already exists, but designed to fit their realities.

And this brings us to a key point: agents don’t replace humans—they augment them. There’s always a developer—usually middle or senior—who oversees the process, makes decisions, and manages integration. Yes, it’s no longer enough to just be able to code. You need to be able to test, configure, and collaborate with autonomous systems.

How to become a team with AI agents

AI advancements allow teams to work at unprecedented speeds. Engineers spend less time writing boilerplate code and more time on architecture, performance, and testing atypical scenarios. We’ve seen this in our own internal agent launches, with productivity gains and cost savings of up to 70% on individual tasks, especially where there are repetitive tasks.

As a result, the very composition of teams is changing. A classic team usually consists of 8–10 developers who work full-time on developing functionality.

According to the World Economic Forum’s Future of Jobs 2025 report, AI and other technologies will displace about 9 million jobs in the next five years. But AI will also create new ones: the same report says that 11 million new jobs will appear by 2030. We are already seeing the emergence of a hybrid role: the AI ​​intelligence engineer. This is the person who is responsible for the interface between human expertise and what the agent produces, managing the agents, validating the results and making sure everything is integrated correctly.

How AI agents can change the workflow

There is a lot of talk about prompt engineering — but it is not where real agentic systems start. Once you move from demo to production, you need to not just write a good query, but be able to set up a reliable workflow. And this is where systems thinking comes into play.

Today, agents still stumble upon:

• bug fixes — it’s hard to track where exactly everything broke: in prompts, tools, or plans;
• Context limits — long documents or multi-step logic overload the model;
• security and cost — agents can expose data or launch uncontrolled API calls.
• task specialization — there is no magic wand like AGI, generalization is weak; even identical queries can yield different results, making reproducibility a real problem.

So what works?

In our experience, specialized agents work best. In one project, we used multiple agents to generate front-end components — but not all at once. Instead of building the entire page, the system divided the task into blocks: one agent analyzed Figma, another generated a mockup, and a third added business logic. At first, this did not speed up development — on the contrary, the architecture with many agents took longer than writing code by hand. But once the logic was ready, the process became reproducible and scalable.

Agents are parts of a system. The engineer’s job is to design the system correctly: decide how to divide tasks, what roles to give agents, and at what point a human should step in and check. The better the system design, the better the contribution agents can make to it.

5 levels of working with AI agents

It is difficult to navigate these transformations without a clear understanding. Blind experimentation is possible, but it will take much more time and effort. We have developed a kind of roadmap that helps the developer level up and measure the level of integration of agents into the development. This allows specialists to determine where they are now — and where they are moving next.

Level 1. Assistant. The role of the person is the author

This is a familiar world with AI assistants and code completion like GitHub Copilot. The AI ​​adds intelligent autocompletion within the context provided by the developer. It suggests lines or blocks of code, but the human is in full control of the process and makes all the decisions.

Level 2. Specialist. The role of the person is delegator

An agent can independently perform a fully defined task on command. For example, generate a set of unit tests for a class or create a Terraform configuration from a description. The task is narrow, but the agent’s autonomy in it is high.

Level 3: Collaborator. Human role: reviewer/architect

The agent system is capable of pulling together a complex, multi-step process with human control at key stages. For example, from a Figma design, generate a ready-made multi-component UI: one agent does the structure, another the styling, and the third is responsible for state control. The person does not write the code, but guides the process and validates the result.

Level 4: Autonomous Team Member. Human Role: Supervisor

This is the level that advanced teams are now trying to reach. An agent can take a full user story or feature and manage its lifecycle: independently planning tasks, writing code, creating tests, consulting documentation via RAG, and submitting pull requests for human approval. The human role shifts to high-level review and strategic architectural decisions, as in a senior tech lead.

Level 5: Agentic Team. Human Role: Product Visionary

This is a long-term vision. A group of interconnected specialized agents can pull together an entire epic or even a small product. A human—the product manager—sets the business goals and constraints, and the agent system plans, designs, tests, secures, and deploys the solution. Humans are responsible for the «what» and «why,» agents for the «how.»

Reaching the latter two levels requires not only overcoming technical challenges, but also fundamentally rethinking roles, team structures, and the very concept of software development. But for those who are ready, the path is clear.

How to stay relevant when just writing code is no longer enough

As agent-based systems take on more engineering tasks, the role of the developer is changing—but not disappearing. They are moving into new roles that require deeper judgment, a better understanding of the system, and the ability to work with the machine, not against it.

Two main trajectories are emerging. Some engineers focus on interacting with agents—learning to delegate tasks, validate results, and interact with them. Others go deeper—building agents themselves, working on the underlying logic, structure, and scheduling systems. Both directions are critically important, but differ in their mindset and skill level.

Relying on agents without knowledge of the code is not an option. You need a strong engineering base to evaluate and correct their output. A developer who does not understand the system from the inside cannot safely use what the agent has generated. Therefore, those who really break through in this area are usually middle+, with domain expertise and practical experience.

The starting point is simple: learn by doing. Start with experiments — use agents to support everyday coding tasks.