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Agentic AI Explained: LLMs Use Tools in an Iterative Loop

By PulseAugur Editorial · [1 sources] ·

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.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Agentic AI Explained: LLMs Use Tools in an Iterative Loop

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The item explains a concept (agentic AI) using examples, rather than announcing a new development.
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  1. dev.to — LLM tag TIER_1 English(EN) · Jesse Ni ·

    ELI5 - What is agentic AI?

    <p>Currently, mainstream agentic AI systems include closed source platforms such as Claude Code, Codex, Cursor, and open source platforms such as Opencode or Kilocode. How do these systems work then?</p> <h1> Standard LLM APIs </h1> <p>This is what happens what you directly use L…

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