From answering questions to finishing tasks
For years, "using AI" in software development meant asking a chatbot for a code snippet and pasting it in yourself. That era is ending. The current generation of coding agents — tools like Claude Code, GitHub Copilot's agent mode, and Cursor's composer — don't just suggest code. They read a codebase, plan a change, write it, run tests, and iterate.
This is a different category of tool, and it changes how engineering teams should think about their workflow.
What actually changed
Context, not just completion
Earlier AI coding tools worked one function at a time, with no real understanding of the surrounding system. Agentic tools read across files, follow imports, understand your existing patterns, and make changes consistent with how the rest of the codebase already works.
Multi-step execution
An agent can now take a task like "add pagination to this API endpoint and update the frontend to use it" and actually do both halves — not just describe how you'd do it. It writes the code, runs the build, and reports back with what changed.
A verification loop
The best agentic tools don't just generate code and stop. They run the test suite, read the error output, and correct their own mistakes before handing the result back to a human. That loop is what makes the difference between a toy demo and a genuinely useful tool.
What this means for engineering teams
Junior-level tasks get compressed, not eliminated. Boilerplate, straightforward CRUD endpoints, and routine refactors are increasingly agent territory. That doesn't remove the need for junior engineers — it changes what they spend their time on, shifting toward review, architecture, and understanding why a change is correct, not just that it compiles.
Code review becomes more important, not less. When an agent can generate a plausible-looking pull request in minutes, the discipline of actually reading and understanding what changed matters more than ever. Teams that skip this step accumulate debt they can't see coming.
The bottleneck moves upstream. When writing code gets faster, the limiting factor becomes clear requirements, good architecture decisions, and knowing what to build in the first place — not how fast you can type it.
What hasn't changed
Agents are excellent at well-specified, bounded tasks with a clear definition of "done" — a failing test to fix, a documented API to implement, a UI component matching an existing design system. They're still unreliable at open-ended architectural decisions, judgment calls about tradeoffs, and understanding business context that isn't written down anywhere.
The teams getting the most out of these tools treat them as capable collaborators that need clear direction and review — not as a replacement for engineering judgment.
We use agentic coding tools daily in our own development work — happy to share what's worked and what hasn't if you're evaluating them for your team.