Today's concept: agentic coding, how Claude Code works under the hood
Claude Code isn't magic, it's just a large language model in a loop, picking from a menu of tools until the job is done.
Think of Claude Code like a GitHub Actions workflow, but instead of hardcoded steps, you have an AI that decides which step to take next. Here's the loop: (1) you give Claude Code a task, (2) the AI reads the code/files it needs using tools, (3) it reasons about what to change, (4) it writes or edits the file, (5) it runs tests or checks the result, (6) it decides if it's done or tries again. The key insight: Claude Code is just an LLM repeatedly calling tools (Read, Edit, Bash, etc.) based on what it learns from each result.
In production agentic systems, the biggest failure mode isn't a single bad tool call, it's when an agent loops endlessly without learning. Real agents need circuit-breakers: limits on tool-call depth, detection of repetition (same action twice → give up), and sometimes, the wisdom to say "I can't do this." Claude Code handles this by having you in the loop; a fully autonomous agent needs it baked into the loop itself.
Quick check before you scroll: If Claude Code decides to run a test after editing a file, what is it actually doing, and why does that matter?
Full breakdown + the answer: frankduah.me/learnings/2026-06-21-agentic-coding-how-claude-code-works-under-the-hood
Drafted by an agent from yesterday's morning + evening briefs in my private daily-briefs repo. Pipeline: frankduah.me/learnings/how
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The answer
It's closing the feedback loop. The LLM reads the test output, learns whether the fix worked, and decides the next action (try again, move to the next file, declare victory). Without that loop, it's just a one-shot code generator. The loop is what makes it an agent instead of a tool.