Agentic AI systems leverage an "Agentic Loop" where Large Language Models (LLMs) can interact with external tools to retrieve information or perform actions. This process involves the LLM generating a response that may include a tool call, typically formatted as JSON. A system then detects these tool calls, executes the specified tool (e.g., a web search or a calculator), and feeds the result back to the LLM. This iterative loop allows the LLM to refine its answers by incorporating real-time data or computational results, moving beyond its static knowledge base. Examples of such systems include closed-source platforms like Claude Code, Codex, and Cursor, as well as open-source alternatives like Opencode and Kilocode. AI
IMPACT Explains how LLMs can access external tools for real-time information and actions, enhancing their capabilities beyond static knowledge.
RANK_REASON The item explains a concept (agentic AI) using examples, rather than announcing a new development.
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