Autonomous LLM Agents: Function Calling, Tool Use, and ReAct Pattern

AI models are transforming from passive chat assistants into proactive software agents. By equipping Large Language Models with **Function Calling** and the **ReAct (Reasoning + Acting)** framework, models solve problems autonomously.

ReAct Framework Execution Loop

1. **Thought**: LLM analyzes user prompt and current state.
2. **Action**: Selects dynamic tool (e.g. SQL query, Web Search API, Calculator).
3. **Observation**: Renders action output back into context for next reasoning step.

# ReAct Loop Pseudocode
while not task_completed:
    thought = llm.generate_thought(history)
    action = llm.select_tool(thought)
    observation = execute_tool(action)
    history.append((thought, action, observation))