An AI agent is just a loop: context, model, tool execution, and repeat
Pavan_Belagatti · x · 2026-07-23
The post argues that an agent is fundamentally a loop: the model reads the current context, returns one machine-readable decision, code executes that step, and the result is appended back into context before the next call. It emphasizes that the model has no persistent memory beyond the context window, which is why a strong context layer matters so much.
Key points:
- The model should emit structured output, such as JSON, so code can act on it.
- Each call is effectively a fresh function invocation.
- A good context layer plus loop design is central to efficient agent execution.
- This is presented as a foundation for better AI agent best practices.
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