AI Agent / Agentic AI

Full definition

Modern agents combine an LLM as the planner/reasoner with tool use (function calling, code execution, web browsing, file operations), memory, and a control loop. Frameworks include OpenAI's Responses API, Anthropic's Claude with tools, LangGraph, and Microsoft Agent Framework (the successor to AutoGen). The 2024–2025 wave of "agentic AI" is moving from demos to production in coding (Cursor, Devin), customer support, research, and operations workflows.

Why it matters

Agentic AI is the next operating step beyond chat. Where a chatbot answers a question, an agent completes the task — drafting and sending the email, opening the ticket, running the analysis. BCG estimates that effective AI agents can accelerate business processes by 30% to 50%, with early adopters seeing 20% to 30% faster workflow cycles.

Example

An engineering team uses a coding agent to handle bug-fix tickets end-to-end: read the ticket, open the repo, run tests, reproduce the bug, write a fix, run tests again, open a pull request — with humans reviewing only the PR.

Next steps

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Related terms

• Large Language Model (LLM)
  A Large Language Model (LLM) is a deep neural network — almost always a transformer — trained on hundreds of billions to trillions of words to predict the next token, and to generate, summarize, translate, or reason over text.
• Tool Use (LLM)
  LLM tool use is the pattern in which a language model acts through external functions, APIs, databases or code execution: the model requests a tool, the host application runs it and returns the result, and the model continues, turning a text generator into a controller that can act. Function calling is the structured-output mechanism most tool use is built on.
• Chain-of-Thought (CoT)
  Chain-of-Thought (CoT) is a prompting and training technique in which a language model is encouraged to reason step-by-step before producing its final answer — improving accuracy on math, logic, and multi-hop reasoning in large models (in Wei et al., 2022, GSM8K math accuracy more than doubled for the largest GPT and PaLM models).
• Context Window
  A context window is the maximum amount of text — measured in tokens — a language model can read and reason over in one inference call, equivalent to the model's working memory for that turn.
• Retrieval-Augmented Generation (RAG)
  Retrieval-Augmented Generation (RAG) is a technique where a language model retrieves relevant documents from an external knowledge base before generating a response, which reduces hallucination and enables citation of sources.

More deployment & operations terms

• Knowledge Graph
  A knowledge graph is a structured representation of entities (people, products, customers, drugs) and the relationships between them, stored as a graph and used to ground AI systems in verified, queryable facts rather than free-form text.