Agentic Coding Is Fast. Your Judgment Is the Moat
Agentic coding shifts developers toward strategy and adds decision fatigue. For Sri Lankan builders, taste, community, and mentorship are the real edge.
Agentic coding is getting fast enough that writing the code is no longer the hard part. The hard part is deciding what to build, judging whether the AI's output is any good, and not burning out from making a hundred small calls a day. That is the tension underneath a recent Stack Overflow Podcast episode, "Developers who move fast still need to do it together", recorded at Microsoft Build 2026.
The guest is Cassidy Williams, Senior Director of Developer Advocacy at GitHub and a former host of that same podcast. Her argument is simple and, I think, correct: as AI takes over the routine typing, the parts of the job that stay human — taste, community feedback, mentorship — get more valuable, not less. For a solo builder or a small team in Sri Lanka, that reframes what you should actually be practising.
🔍 What "shifting to higher-level strategy" really costs you
When an agent can scaffold a feature in one prompt, your day stops being about how do I write this and becomes is this the right thing, and is this output correct. The podcast frames this as a shift toward higher-level strategy. The part people undersell is the price tag attached: decision fatigue.
Every generated diff is a decision. Accept, reject, or reprompt. Do that 80 times before lunch and your judgment gets worse, not because you're lazy but because deciding is metabolically expensive.
Key takeaway: The bottleneck moved from typing speed to decision quality. If you don't manage your decisions, the AI just helps you make bad calls faster.
A few concrete habits that help:
- Batch the trivial decisions. Let the agent handle formatting, boilerplate, and test stubs without asking you each time.
- Reserve real attention for architecture and data models — the things that are expensive to undo.
- Write the acceptance criteria before you prompt. If you can't say what "good" looks like, you can't review the output.
⚡ Where the human parts actually live
Williams points at three things that AI doesn't replace: human taste, community feedback, and mentorship. That sounds soft until you notice it's exactly the stuff a code model can't give you.
| Skill | Can an agent do it? | Why it stays human |
|---|---|---|
| Generate a CRUD endpoint | Yes, in seconds | It's a solved, patterned problem |
| Decide the endpoint is even needed | No | Requires product and user context |
| Tell you your API design is confusing | Rarely, honestly | Needs a peer who'll disagree with you |
| Help you grow past your current level | No | Mentorship is relational, not generated |
Taste is what tells you the generated solution is technically fine but wrong for your users. You build taste by reviewing other people's work, reading real codebases, and shipping things that get feedback. None of that is downloadable.
🌐 The "do it together" part matters more in Sri Lanka, not less
The title's point — moving fast still needs to be done together — lands harder when you're far from the big tech hubs. If you're a student in Colombo or a two-person shop in Kandy, the AI is the same one a San Francisco engineer uses. The gap is no longer tooling. It's the network around the tooling.
The playing field for writing code just flattened. The playing field for taste and feedback did not. That gap is now the whole game.
Practical, free ways to close it from here:
- Join a community that reviews work, not just chats. A Discord where people paste PRs beats one where people paste memes.
- Contribute to open source. A stranger's code review on your pull request is free mentorship from someone senior.
- Publish what you build. Feedback only shows up after you ship something public.
- Pair, even async. Record a short Loom-style walkthrough of a decision and ask one person to poke holes in it.
🛠️ The new GitHub Copilot app, and what to check before you commit
The episode also references new GitHub Copilot announcements from Microsoft Build 2026, including a new GitHub Copilot app. The podcast doesn't publish the full spec — it points to GitHub's own product news for details — so I'm not going to invent version numbers or features it didn't state.
What I'd tell any Sri Lankan builder is this: before you standardise on any AI coding assistant, run the numbers in LKR and compare the actual limits, not the marketing.
- Assistants price in USD; a "$10/month" plan is real money against a local salary or a student budget.
- Free tiers, request caps, and model quality differ a lot between tools.
- The right pick for a hobby project is often not the right pick for a client codebase.
If you want to compare the options side by side, I built two small tools for exactly this: an AI coding assistant comparison for features and limits, and an AI coding assistant cost calculator to see the monthly bill in rupees before you subscribe.
💡 What this means for you
If you take one thing from the episode, take this: the skill that used to define a good developer — writing correct code quickly — is now partly automated, and that's fine. It frees you to compete on the things that were always harder to teach.
- Practise judgment, not just syntax. Review AI output like you'd review a junior's PR.
- Protect your decision budget. Fatigue is now a real engineering risk, so automate the small calls and save your focus.
- Invest in people. Find a community that gives honest feedback, and mentor someone a step behind you — teaching sharpens your own taste.
- Run the cost in LKR before you commit to any assistant.
The tools got faster. That's not the story. The story is that speed is now cheap and judgment is expensive, and judgment is the one thing you still build the old way: slowly, with other people, by caring whether the thing you shipped was actually good.