A few months ago, Mark Zuckerberg predicted that most coding will soon be done by AI — not just typing, but the full loop: setting goals, running tests, fixing bugs, and writing better code than top engineers. We’re already watching this unfold. Large language models (LLMs) are no longer just autocomplete — they’re becoming autonomous agents. They plan, reason, trigger workflows, and interact with APIs. They’re starting to behave like junior developers. This shift is both exciting and unsettling. If your day-to-day involves writing glue code, dashboards, or data transformation scripts, there’s a good chance an agent can already do it faster. But does that mean you’re out of a job? Yes, some tasks are going away. But no, you’re not out of the game. Your role just needs to change. In this piece, we’ll share what we’re learning from real-world engineering projects — and why this shift, while it may be uncomfortable, is a massive opportunity for developers ready to evolve. This article was created in collaboration with my colleague — Nazarii Drushchak, Data Scientist at SoftServe. This article was created in collaboration with my colleague — Nazarii Drushchak, Data Scientist at SoftServe. Is Human Coding Dying? At Microsoft, 30% of code is now written by AI. 91% of organizations are already using AI agents, with task automation as the most common use case, according to the survey by Okta. Such data leads to a simple question: Is this the beginning of the end for human programming? written survey Not quite. Even the best AI struggles with large-scale systems, ambiguous requirements, and evolving context. Agents lack judgment, domain insight, and the ability to reason across multiple layers of architecture. But one thing is clear: the role of the engineer is changing. Agents can accelerate productivity, handle boilerplate tasks, and serve as tireless collaborators. The job won’t vanish — but it will look very different, very soon. From what we’ve seen at SoftServe, agentic AI is reshaping the development process. We’ve integrated agents to assist in generating initial backend logic, fully-functional UI, CI/CD configurations (including Terraform), and even early-stage requirement artifacts. These agents don’t build complete systems, but they generate first-pass solutions based on available data like PRDs. The result isn’t perfect, and we don’t expect it to be. But it’s good enough to move engineers past the blank page, providing structure they can iterate on — shifting the role from writing every line to shaping and refining agent output. There’s a rise of agent-powered tools across the industry. Cursor, for example, helps developers iterate directly in their IDE. Lovable and V0 assist in building UI components, while platforms like Windsurf explore more complex composition. Each of these tools offers partial automation — but many remain closed-source and opaque. You can’t control how they behave or adapt them to your stack. That’s why many companies often build their own internal agents, inspired by what’s out there, but designed for the realities of delivery. And this leads to an important point: agents aren’t replacing people — they’re augmenting them. There’s always a developer — typically at a mid to senior level — overseeing the output, making judgment calls, and driving integration. Yes, it’s not enough to know how to write code anymore. You have to know how to review, guide, and collaborate with autonomous systems. How To Make AI Agents Your Teammates This change unlocks significant speed. Engineers spend less time on boilerplate and more time on architecture, performance, and edge-case handling. We’ve seen this firsthand with our own internal agent deployments — productivity and cost gains of up to 70% on specific tasks, especially where reusable patterns are involved. As a result, the shape of engineering teams is changing. Traditional teams might include 8–10 developers focused full-time on building features. In the agent-augmented model we’re now piloting, that same team might have five human engineers — and a set of agents generating stubs, tests, configs, or documentation. We call it “one-pizza team.” According to the World Economic Forum's 2025 Future of Jobs report, 9 million jobs are expected to be displaced by AI and other emergent technologies in the next five years. But AI will create jobs, too: The same report says the technology will lead to some 11 million new jobs by 2030. We’re already seeing the emergence of a new hybrid role: the intelligence engineer. 2025 Future of Jobs report intelligence engineer This person will own the interface between human insight and agent output, guide the agents, validate results, and ensure everything integrates cleanly. Designing Workflows with AI Agents Prompt engineering gets attention — but it’s not where real agentic systems begin. The moment you move from demo to deployment, the challenge shifts from writing good prompts to designing resilient workflows. That’s where system-thinking comes in. Building useful agents isn’t about getting one answer right; it’s about coordinating multiple agents, tools, and tasks in a controlled, auditable way. And that’s harder than it sounds. Agents today still struggle with: Debugging — tracing failures across prompts, tools, and plans is difficult.
Context limits — long documents and multi-step logic overwhelm models.
Security and cost — agents can leak data or trigger runaway API calls. Debugging — tracing failures across prompts, tools, and plans is difficult. Debugging Context limits — long documents and multi-step logic overwhelm models. Context limits Security and cost — agents can leak data or trigger runaway API calls. Security and cost Challenges with task-specific agents remain — there's no 'silver bullet' like AGI right now. Generalization across domains is poor. Even identical prompts can produce different outputs, making reproducibility a real challenge. So, what works? From what we’ve seen, agents work best when they’re specialized. In one project, we used several agents to generate frontend components — but not all at once. Instead of building a full page in one go, the system broke the task into blocks: one agent analyzed Figma, another generated layout structure, and a third added business logic. It wasn’t faster upfront — in fact, building the architecture for this multi-agent pipeline took more time than coding manually. But once it was in place, the process became repeatable and scalable. Agents are parts of a system. The engineer’s job is to structure that system well: decide how to split tasks, define agent roles, and know where human review is needed. The better we design that system, the more reliably agents can contribute. The Five Levels of Agentic Maturity The Five Levels of Agentic Maturity To navigate this transition, it’s not enough to just experiment; you need a map. Inspired by the levels of autonomous driving, we at SoftServe have developed a maturity model to understand and benchmark the integration of agents into software engineering. This can help developers define where they are, and more importantly, where they are going. 🏁 Level 1: The Assistant Level 1: The Assistant This is the familiar world of AI-powered code completion and suggestions, like GitHub Copilot. The AI acts as a smart autocomplete, operating within the developer's immediate context. It suggests lines or blocks of code, but the human is entirely in control, making all decisions and driving the workflow. Human Role: Author. Human Role: 🏁 Level 2: The Specialist Level 2: The Specialist Here, an agent can reliably execute a complete, well-defined task on command. Think of an agent that can generate a full suite of unit tests for a given class or create a Terraform configuration file from a simple description. The task is narrow, but the agent’s autonomy within that task is high. Human Role: Delegator. Human Role: 🏁 Level 3: The Collaborator Level 3: The Collaborator A system of agents can handle a complex, multi-step workflow with human oversight at key milestones. For example, taking a Figma design and generating a fully-functional, multi-component UI, with one agent handling structure, another handling styling, and a third handling state management. The human doesn’t write the code but guides the process and validates the output. Human Role: Reviewer/Architect. Human Role: 🏁 Level 4: The Autonomous Teammate Level 4: The Autonomous Teammate This is the current frontier for advanced agentic engineering. At this level, an agent can take a complete user story or feature requirement and manage its own lifecycle. It can independently plan the necessary tasks, write the code, create tests, consult documentation via RAG, and submit a pull request for human approval. The human’s role shifts entirely to high-level review and strategic architectural decisions, much like a senior tech lead. Human Role: Supervisor. Human Role: 🏁 Level 5: The Agentic Team Level 5: The Agentic Team This is the long-term vision. A swarm of interconnected, specialized agents can take on an entire product epic or even a small project. A human product owner sets the business goals and constraints, and the agentic system autonomously coordinates to plan, develop, test, secure, and deploy the solution. Humans are responsible for the 'what' and 'why,' the agents handle the 'how.' Human Role: Product Visionary. Human Role: Understanding this progression is key. Most of the industry is at Level 1, with pockets of excellence exploring Level 2 and 3. Reaching Levels 4 and 5 isn't just a technical challenge — it requires a fundamental rethinking of roles, team structures, and what it means to build software. But for those who embrace it, the path is clear. How to Stay Relevant When Agents Start Writing Code As agentic systems take on more engineering tasks, the role of the developer is changing — but it’s not disappearing. Developers shift into new roles that require deeper judgment, better system understanding, and the ability to work with — not against — machine collaborators. There are two broad paths emerging. Some engineers focus on operating agents — learning how to delegate tasks, verify results, and collaborate. Others go deeper, building agents themselves, working on the underlying logic, structure, and planning systems. Both are essential, but they require different mindsets and skill levels. Relying on agents without knowing how to code doesn’t work. You still need strong engineering fundamentals to evaluate and correct outputs. A developer who doesn’t understand the underlying system can’t safely use what the agent produces. That’s why the engineers we see succeed in this space are typically mid-level or higher, with domain-specific knowledge and hands-on experience. The starting point is simple: learn by doing. Begin by experimenting, using agents to support everyday coding tasks.

Your Old Job Is Gone — But You Can Learn a Better One Mastering Agentic AI

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