Large language models (LLMs) have become indispensable for tasks such as writing and question answering because they can converse in natural language and reason about the world. Their outputs already shape our decisions and actions.\ \ What changes when an LLM is given the ability to act directly in the real world? What new scenarios emerge when multiple LLMs can interact with each other?\ \ Agentic LLMs combine language understanding with actionable execution. They translate textual instructions into concrete operations via API calls, robot controllers, or script execution, and multiple agents can exchange information through message queues or shared memory to cooperate on complex tasks.\ \ For users, this means issuing a "please do X" command directly to the model without writing code or performing manual steps. For developers, it raises concerns about safety constraints, action verification, and protocol design for multi‑agent communication.\ \ Core technical components\
- Mapping model output to action commands (e.g., JSON, shell scripts)\
- Execution layer: API gateway, robot controller, or local script environment\
- Multi‑agent interaction: shared memory, message bus, negotiation protocols\
- Safety mechanisms: action audit, permission checks, rollback strategies\ \ These components form a closed loop of perception, decision, and execution, enabling autonomous operation across the three stages.\ \ Potential applications span automated customer service, smart home control, data extraction and processing, and cross‑model collaborative reasoning. As safety frameworks mature, Agentic LLMs are poised to become the next bridge between humans and machines.\ \ Review