Copilots suggest code; Agents write code, run the code, read the errors, and rewrite the code. Welcome to the era of autonomous execution.
1The ReAct Architecture
An Agent is not a massive neural network; it is a standard LLM placed inside a Python/Node script that runs a while loop. The script prompts the LLM to 'Think', 'Act', and 'Observe'. If the LLM generates bad code and the terminal throws an error, the script feeds that error back into the LLM as an 'Observation'. The LLM 'Thinks' about why it failed, 'Acts' by rewriting the file, and 'Observes' the new result. This loop continues until the task is marked Complete.
2Tool Use (Function Calling)
LLMs are isolated text generators. To affect the real world, they use 'Function Calling'. You provide the LLM with a JSON list of capabilities (e.g., executeBash, readFile, gitCommit). The LLM does not run the code; it outputs a JSON string saying {'function': 'executeBash', 'args': 'npm run build'}. The wrapper script parses this JSON, runs the bash command on your computer, and returns the terminal output to the LLM.
3Human-in-the-Loop (HITL)
Agents are highly prone to getting stuck in infinite loops (hallucinating the same broken fix 100 times) or accidentally executing destructive commands. Therefore, production-grade Agents use HITL. When an Agent requests to use a dangerous tool (like git push or DROP TABLE), the wrapper script intercepts the request, pauses the loop, and displays a UI prompt to the human developer. The human must click 'Approve' or 'Deny' before the script executes the command.
