AI Is Reducing Context Switching, But Experience Still Matters

Intro

Context switching has always been one of the most expensive parts of my work. Every time I move from one task to another, I don’t just lose minutes, I lose the mental model I was building. AI has helped me reduce that fatigue by keeping track of ideas, summarizing progress, and helping me resume work without starting from zero every time.

And then, a sudden bug appears

It happens from time to time. A specific edge case appears and tests the stability and reliability of the applications you created. But you are already working on that shiny new feature customers are waiting for, usually under a tight deadline.

With AI agents, you can tackle some bugs during the day and still have time to work on your feature. AI agents do not remove context switching completely, but they reduce the amount of context you need to rebuild by yourself. Reviewing large amounts of code has always been tedious, especially when you start working in a new codebase.

This is the new version of context switching: not jumping between tasks alone, but coordinating with AI to recover context faster.

Stop rebuilding context manually

If you are prepared, all you need are your logs, your codebase, and maybe the name of a function or file. Sometimes, that’s enough. Just create a new session in your preferred AI agent and pass in the required logs, file names, or whatever you remember about the process. AI nowadays is smart enough to find the code. You can have a natural conversation, ask when specific parts of the code were implemented, check back on that change, connect it to your ticket, and ask for more context about it.

In the past, understanding an unfamiliar piece of code often meant finding the person who had worked on it the most. Now, if your tools are connected, you can ask when the code was written, which tickets were related to it, what documentation supported it, and why certain decisions were made at the time.

Experience still matters

I see AI agents as big readers and summarizers. But don’t lie to yourself: you absolutely need technical knowledge and experience to know when the agent starts hallucinating. Your logs should be well prepared when you are dealing with emergencies. Also, having a well-tested application helps you protect the most important business cases, especially the ones users depend on every day.

I still face situations where writing a function by hand or fixing a bug takes way less time than having a discussion with the agent. But I see myself using the editor less every day for new features, and spending more time coordinating how to organize files, define the architecture, and maintain traceability in the applications I write.

Where are we heading?

The future of development may be less about writing every line of code yourself and more about making sure agents have the right context: tickets, codebases, architecture diagrams, tests, and product constraints. If agents have that context, they can start solving many coding tickets.

We will spend more time coordinating the product, testing it, thinking about edge cases, and preparing systems to work at a larger scale, rather than doing the coding itself.

Local models could become an alternative solution in the battle against token and inference costs.

Should I still code by myself?

You should. Whenever you have the chance, practice your coding skills. You can still contribute to open source and practice LeetCode. Interviews still require some of these skills.

It is like knowing how to drive manual, even if you use automatic most of the time. You may not need the skill every day, but when the situation demands it, the skill still matters.

Day to day, companies will continue investing in AI tools. Developers who know how to use those tools well, and who still understand the fundamentals, will move faster than those who depend on AI blindly.