Foundations8 minLesson 1 of 60

What Agentic Coding Actually Is

There is a real difference between chatting with a model and handing a task to an agent. A chat assistant answers the question in front of it and stops. A coding agent runs a loop: it reads your project, decides on an action, runs that action, looks at the result, and then decides what to do next. It keeps going until the job is done or it gets stuck.

That loop is the whole point. People often call it the agentic loop: gather context, take an action, verify the result, repeat. When you understand the loop, most of the advice in this course stops feeling like a list of tricks and starts feeling obvious.

The agentic loop

  1. Gather context: read the relevant files, run a search, check the failing test.
  2. Take action: edit a file, run a command, call a tool.
  3. Verify: run the test, read the output, check the diff against the goal.
  4. Repeat: use what it learned to decide the next action, or stop when done.
Why this matters
If you give an agent no way to verify its own work, it cannot close the loop. The single biggest upgrade you can make to most setups is giving the agent a fast, honest signal: a test command, a type check, a linter. Feedback is fuel.

Chat assistant vs coding agent

TraitChat assistantCoding agent
ScopeOne replyMulti-step task
ToolsNone or fewFiles, shell, search, more
VerificationYou check itIt checks itself
Best forQuestionsShipping changes

A simple first session

zsh - my-app
# point the agent at a real, scoped task
$claude
> fix the failing test in src/auth and explain the root cause
✓ read 3 files, ran the suite, found a null check, patched it
✓ tests pass (12/12). diff ready for review.
$

Notice the agent did not need a clever prompt. It needed a clear goal and a way to confirm success. That is the foundation everything else is built on.

Anthropic Academy: Claude Code 101Free first-party course that walks through the agentic loop and the explore, plan, code, commit workflow.anthropic.skilljar.com
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