Agentic AI
Also known as: Agentic systems, Autonomous AI
AI systems that act autonomously over many steps to achieve a goal, combining planning, tool use and memory.
Agentic AI is the umbrella term for AI systems that act with autonomy — making decisions, calling tools, and adjusting their plan as they go. A standard chatbot replies once; an agentic system can spend minutes or hours pursuing a goal, with the LLM acting as the reasoning core inside a larger loop.
Agentic systems are typically built from four components: a planner (the LLM choosing the next step), tools (APIs, code execution, web access), memory (short-term scratchpad and long-term vector store), and a controller that decides when to stop. Frameworks like LangGraph, OpenAI Agents SDK and the Vercel AI SDK make this loop reusable.
Agentic AI is the fastest-growing segment of the AI stack in 2026 because most economic value comes from completing tasks, not chatting about them.
See also on SoftPerceptron
Related terms
- AI Agent
An LLM-based system that can plan, use tools and take multi-step actions toward a goal — not just answer a single prompt.
- Large Language Model (LLM)
A neural network trained on massive text corpora to predict the next token, used for chat, coding, reasoning and as the brain inside AI agents.
- Model Context Protocol (MCP)
An open standard from Anthropic for connecting LLMs to external tools and data sources through a uniform server interface.