Agentic Loop
An agentic loop is the cycle an AI agent follows: observe the current state, think about what to do, take an action, and observe the result — repeating until the task is complete. This loop is what makes AI agents autonomous rather than single-shot: they adapt based on outcomes rather than blindly generating code.
Example
The agentic loop is the engine that powers autonomous AI development. It's what separates a simple code generator from an agent that can complete entire tasks.
The Loop
┌──────────────────────┐
↓ |
Observe → Think → Act → Evaluate
|
Done? ──No──┘
|
Yes
↓
Complete
Loop Stages
| Stage | What Happens | Example |
|---|---|---|
| Observe | Read current state | Read file contents, error messages |
| Think | Plan next action | "I need to fix the type error on line 23" |
| Act | Take action | Edit the file, run a command |
| Evaluate | Check result | Did the tests pass? Did the error resolve? |
Good vs Bad Loops
Effective loop:
- Each iteration makes progress
- Agent learns from failures
- Knows when to stop
- Escalates when stuck
Problematic loop:
- Repeats the same fix over and over
- Makes changes without checking results
- Doesn't know when to ask for help
- Gets stuck in infinite cycles
Why Loops Matter for Vibe Coding
Single-shot code generation gives you an answer. An agentic loop gives you a working solution. The loop keeps iterating until the code actually runs, tests pass, and the feature works.
Recognizing the Loop
When you use Cursor's agent mode or Claude Code, watch for the loop:
- AI reads your codebase (observe)
- AI explains its plan (think)
- AI edits files and runs commands (act)
- AI checks for errors (evaluate)
- If issues remain, it loops back
Understanding this pattern helps you know when to intervene and when to let the agent work.
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