What Actually Makes an LLM an Agent: The Loop, Tools, and MCP Explained

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A large language model on its own only converts text to text, with no ability to access live data or take real-world actions. An agent changes this by placing the model inside a loop that lets it call external tools — such as APIs or databases — and use the results to decide its next step. Experts distinguish between an agent definition, which acts as a reusable template holding instructions, tool policies, and limits, and an agent instance, which is a live running occurrence carrying a specific goal and consumed resources. The Model Context Protocol (MCP) standardizes how an agent host discovers and calls tools, eliminating the need for custom integrations with each individual data source. MCP effectively handles the tool-communication layer, while the agent loop, its policies, and its termination conditions must still be built and managed on the host side.

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Anthropic's Jacobian Lens Repurposed to Improve LLM KV Cache Memory Management

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