What is agentic coding? Beyond autocomplete

A couple of years ago, "AI for coding" meant autocomplete — the editor guessing your next line while you typed. Useful, but you were still the one doing the work. That's not what people mean anymore. The term now is agentic coding, and it's a different thing entirely: an agent that takes a task and runs it, end to end, while you direct.

If you're trying to make sense of the shift, here's what agentic coding actually is, how it differs from what came before, and what it takes to do it well.

From autocomplete to agents

Autocomplete is a suggestion engine. It completes the line, you accept or reject, and you stay in the driver's seat keystroke by keystroke. The AI is a faster set of fingers.

Agentic coding moves the AI from suggesting to acting. You say what you want built. The agent reads the relevant files, makes a plan, edits across several files, runs the tests, sees a failure, fixes it, and comes back with working code. You weren't typing through any of that. You were directing and reviewing.

That's the leap: from a tool that helps you type to a partner that does the task.

What "agentic" actually means (the loop)

The word "agentic" points at one thing: the loop. An agent doesn't produce one answer and stop. It runs a cycle — gather context, take an action, check the result, react, and go again until the work is done or it's stuck. The best workflows lean into this — as the practitioners pushing beyond autocomplete describe it, the agent's value is in running that gather-act-verify loop autonomously, not in any single suggestion.

Stuart Leo

Autocomplete answers once. An agent runs a loop — act, check, react, repeat — and that loop is the whole difference.

You see it live in a terminal-based agent: read a file, plan, run a command, hit an error, try a fix, re-run. That visible loop is "agentic" in action.

What changes for you

When the AI starts running the loop, your job changes shape:

• You stop writing most of the code and start writing the brief — what to build, what to avoid, how you'll know it worked.
• You stop measuring progress in lines typed and start measuring it in decisions made and outcomes shipped.
• You become the reviewer and the standard-setter, not the typist.

This is a genuine adjustment, and it's where people either thrive or flounder. Treat the agent like fancy autocomplete and you'll micromanage it into mediocrity. Treat it like a capable crew member you direct, and it flies.

What it needs to work

Agentic coding isn't magic, and it fails predictably without three things:

1. Context. The agent needs to know how your project works — decisions, patterns, constraints. Without it, it builds something plausible and wrong.
2. A checkable target. "Improve checkout" gives the loop nothing to verify against. "Bookings past capacity are refused, and the tests pass" gives it a finish line.
3. Your direction. The loop runs the work, but you set the course and own the quality bar.

Give it those and the loop produces real, reviewable software. Skip them and you get fast, confident drift.

Where to start

Pick an agent, put your project in git, and try one small, well-scoped task — with a clear brief and a way to check the result. Watch the loop run. Then write down what you learned so the next task starts ahead. That habit of capturing context is what turns a one-off win into a way of building.

Agentic coding moves you from writing code to directing an agent that writes, runs and checks it. The skill that matters isn't typing faster — it's directing well.

Start here: run your first session, see what an agent harness is, or learn the Pilot model.