Agentic AI
Adjective · Noun · AI & Machine Learning · Origin: 2024
Definitions
Agentic AI refers to artificial intelligence systems designed to autonomously pursue complex goals by planning, reasoning, and taking actions with minimal human oversight. Unlike traditional AI that responds to single prompts, agentic systems can break down objectives into subtasks, use tools, browse the web, write and execute code, and iterate on their own outputs. They maintain context across multiple steps and can adapt their approach when encountering obstacles. Common frameworks for building agentic AI include LangChain, AutoGPT, and CrewAI. The approach is gaining traction in software development, research automation, and business process workflows. Key challenges include maintaining safety guardrails, preventing unintended actions, and ensuring the agent stays aligned with the user's actual intent throughout extended autonomous operation.
In plain English: AI that can independently carry out complex tasks — not just answering questions but actually doing things like booking flights, writing code, or managing projects.
Used as an adjective to describe any workflow where an AI system operates with a degree of autonomy — making tool calls, browsing the web, writing and executing code — rather than simply responding to prompts in a single turn.
Example: 'We moved from a simple chatbot to an agentic architecture where the model plans its own research steps and calls our internal APIs.'
Source: architectural pattern
Etymology
- 2023
- AI researchers begin using 'agentic' to describe LLM systems that autonomously plan and execute multi-step tasks
- 2024
- Andrew Ng popularizes the term 'agentic AI' in talks and essays, distinguishing it from simple chatbot interactions
- 2025
- Agentic AI becomes a mainstream product category as major vendors ship autonomous agent frameworks
Origin Story
When AI Learned to Take the Wheel
The term 'agentic AI' emerged around 2023-2024 as researchers and industry leaders needed a word for AI systems that could pursue goals autonomously, making decisions and taking actions without constant human oversight. Unlike chatbots that simply respond to prompts, agentic AI plans, reasons, uses tools, and adapts its strategy mid-task. The concept draws on decades of agent-based research in computer science, stretching back to the 1980s when distributed AI researchers first modeled software 'agents' that could act independently. But the modern usage crystallized as large language models gained the ability to call APIs, write and execute code, and chain multiple reasoning steps together. Andrew Ng, the Stanford professor and AI pioneer, was among the most vocal champions of the term in early 2024, arguing that agentic workflows would be the next major leap in AI productivity. The word spread rapidly through industry keynotes, research papers, and startup pitch decks, becoming one of the defining buzzwords of the generative AI era.
Context: AI research community, 2023-2024
Fun fact: Before 'agentic' caught on, researchers tried terms like 'autonomous AI' and 'self-directed AI,' but 'agentic' won out partly because it sounded less threatening.