An LLM agent is defined by a loop that connects a large language model with tools, allowing it to access external information and perform actions beyond its training data. This loop involves the model deciding when to call a tool, processing its results, and repeating until a task is complete. The concept is further divided into an agent definition, which outlines the template including system context, tool policies, and termination limits, and an agent instance, which represents a specific running occurrence with a user-defined goal and consumed budget. AI
IMPACT Clarifies the core components and definitions of LLM agents, distinguishing between agent definitions and instances, and explaining the role of protocols like MCP in tool integration.
RANK_REASON The item explains a technical concept related to LLM agents and protocols, rather than announcing a new product or research finding.
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