Define Agentic: Meaning, Origin and a Three-Question Test

What agentic means: the senses in one table

Agentic is an adjective made of the noun agent and the suffix -ic, as in heroic. An agent has two meanings that every sense of agentic leans on: someone who acts, and someone who acts for another person, like a travel agent or a secret agent. Which one a sentence uses depends on the field.

The two dates answer different questions. The 1800s is the oldest use of the spelling, in a chemistry sense that has died out; 1966 is Merriam-Webster's first known use of the meaning dictionaries define today. Merriam-Webster's entry, updated on October 24, 2026, lists two senses: "able to accomplish results with autonomy", said of AI or of self-assertive behaviour. Dictionary.com adds the process sense. Collins gives one line: "able to act independently".

The word takes "more" and "most" because it describes a degree, so one system can be more agentic than another. The related noun is agenticness, which OpenAI's researchers used in 2023 for that degree. Merriam-Webster gives the pronunciation as uh-JEN-tik (ə-ˈjen-tik), stressed on the second syllable, while agent is stressed on the first; Dictionary.com and Wiktionary also list ay-JEN-tik.

Is agentic a real word? Where it came from

Yes, and it is far older than the AI boom. What changed in the 2020s is the field that uses it most, and the dates below show the handover.

The 1974 sense is the one that surprises people. In his obedience experiments of the early 1960s, Stanley Milgram had volunteers give what they believed were electric shocks to a stranger on an experimenter's orders, and 65% went all the way in the best-known version. To explain why, he described a shift into an "agentic state", in which "the individual no longer views himself as responsible for his own actions but defines himself as an instrument for carrying out the wishes of others" (Obedience to Authority, page 134). The explanation has not held up well. Arthur Miller, a specialist of the obedience studies, wrote in 2004 that it has "very little empirical support", as Nestar Russell and Raphaël Künstler recall in a 2024 review of the experiments.

The word still shows the two meanings packed into the noun agent. Milgram's agent acts for someone else and gives up their own judgement; today's agentic AI acts for someone else and is supposed to use judgement of its own.

Albert Bandura's 2001 paper put a name on that "for someone else" part: "proxy agency that relies on others to act on one's behest to secure desired outcomes". He also named its price, "a vulnerable security that rests on the competence, power, and favors of others". Delegating a task to an AI agent counts as proxy agency, with the same price, which is why the agents described further down ask for approval before they pay, send or delete. (In the same paper, Bandura called the computer model of the mind "devoid of consciousness and agentic capabilities".)

How the word moved from psychology to computer science

OpenAlex, a free index of more than 300 million research works, shows the handover in titles. The counts below are papers with the exact word agentic in their title; the fields are OpenAlex's own automatic classification.

Books moved earlier. Agentic became more frequent than agentive, its older neighbour from grammar, in Google's corpus of English books in 2017: 0.29 against 0.26 occurrences per million words (Google Books Ngram; the corpus ends in 2022). Searches came last. "Agentic" first rises above zero on Google Trends for the United States in April 2024, a few weeks after Ng's talk. The fall 2025 high that AP reported did not last as a record: searches kept climbing to a peak in June 2026, about two and a half times the October 2025 level, and in October 2026 stood at about 40% of that peak.

What agentic means in AI, from the people who build it

The AI sense needs three terms. A model, or large language model (LLM), is the AI program that reads and writes text, such as GPT or Claude. A tool is an action the software lets the model trigger: run a search, send an email, query a database. A workflow is a fixed sequence of steps written in code, where the model only fills in text at set points.

Labs and researchers define agentic with those pieces, and their definitions agree more than the vendor pages suggest.

Ng explained the choice at LangChain's Interrupt conference in May 2025. People kept arguing whether a given system was "truly" an agent, and he thought the field would do better by saying that systems are agentic to different degrees, calling them all agentic and getting on with it. In his June 2024 letter he added that "mainly only technical people use the word 'agentic' (for now!)"; a year later he said marketers had picked up the term around mid-2024 and started putting it on everything. Willison, before settling on his one-liner, had crowdsourced 211 definitions of "agent" and sorted them into 13 groups.

Where the definitions part ways is the number of agents. Sinan Aral, a professor at MIT Sloan, reserves "agentic AI" for "systems that incorporate multiple, different agents that are orchestrating a task together", while noting that "most people still refer to the two interchangeably". The explainers of Wrike and Moveworks, two software companies whose pages Google shows for these searches, use the same definition. The dictionaries, OpenAI's 2023 paper, Anthropic and Ng apply the word to a single system. A page that says agentic AI means several agents working together is using one camp's definition.

Is agentic just a buzzword?

Partly, and the people who popularised it say so. The word has the precise technical meaning set out above, and a marketing use that stretches it to almost any AI feature.

• The technical word. Researchers adopted agentic in 2023 for a degree of independence that can be described and compared. In June 2024, Ng still saw it as a word "mainly only technical people use", so an article about agentic workflows was "less likely to be marketing fluff".
• The sticker. A year later, Ng said marketers had taken the word around mid-2024 and put it on everything. Gartner calls the relabelling of chatbots, assistants and older automation as agents "agent washing", and estimated in June 2025 that "only about 130 of the thousands of agentic AI vendors are real". Thomas Dietterich, a professor emeritus at Oregon State and a long-time builder of AI assistants, took issue with "agentic" being used for "any action a computer might do, including just looking things up on the web" (AP, November 2025).
• The gap with what agents can do. Andrej Karpathy, a founding member of OpenAI, said in June 2025 that talk of "the year of agents" worried him and that "this is the decade of agents"; in October he told the Dwarkesh Podcast that, as stand-ins for a human intern, agents "just don't work" yet. In TheAgentCompany, a Carnegie Mellon benchmark of simulated office work, the best agent tested in mid-2025 completed 30.3% of the tasks (The Register, June 2025). Willison points to the gap inside the name: humans "have agency… Despite the name, AI agents can do nothing of the sort", since they can't answer for what they did.

So treat "agentic" in a product description as a claim to check rather than a feature. The three questions further down take a few minutes on one run.

Agentic vs agent, autonomous, agentive and generative

Four neighbouring words cause most of the confusion.

Agent, the noun, says less than it seems. Classic AI textbooks call anything that perceives its surroundings and acts on them an agent; Princeton researchers point out in their 2024 paper "AI Agents That Matter" that "a thermostat would qualify as an agent under the traditional definition". So agentic AI and an AI agent are not two products: "AI agent" names the thing, and agentic says how much of the deciding it does. A chatbot with one search tool is a weakly agentic agent; a coding assistant that edits files, runs tests and loops until they pass is a strongly agentic one.

Autonomous measures how long a system runs without a person. Agentic measures who chooses the steps. Chan's paper gives the example: "Many factory robots are highly autonomous… but do not have agency." The reverse also exists. ChatGPT's agent mode chooses its own steps and still stops for "user confirmations for high-impact actions", such as a purchase.

Agentive is a grammar word. It marks the doer of an action, like the -er in lexicographer, and can't replace agentic.

Generative describes what a model produces. A generative model writes text, draws pictures or produces program code when you ask; an agentic system puts such a model in charge of choosing and carrying out actions. Most agentic systems have a generative model at their centre.

The three-question test

The definitions above come down to three questions you can ask about one run of any tool, from a chatbot to a product sold as an "AI employee".

1. Who chooses the next step? If code fixes the sequence and the model writes text at set points, the code is in charge. If the model reads the situation and picks the next action, the model is in charge.
2. Can it act? A model that only returns text is advising. A model that can run a query, send a message or edit a file is acting.
3. Does it keep going until it decides it is done? One pass and a stop is a pipeline, a fixed chain of steps. An agent loop observes, acts, observes again, and stops when the goal is met or a budget runs out. Erik Schluntz, a researcher at Anthropic, puts it as a property of the task: with an agent, "you don't know how many steps it's gonna take to complete".

Three no answers describe a script or a single prompt. If the code chooses and the model writes, you have a workflow, low on the scale. If the model chooses, acts and loops, you have an agent. The answers live in the tool's log, the list of every action it took with its inputs, which is why the test asks about one run rather than about the product page.

The test on a real agent: one goal, two weeks of data

Here is what the three questions look like on a real agent. The test ran on October 27, 2026, with Claude Code 2.1.311, Anthropic's coding agent, used here as a general-purpose agent with the Claude Sonnet 5.5 model. It could read files and nothing else: no command line, no writing, no internet. The data was fictional: two folders about a small software product's signups over two weeks, with the same files in each. A README file describes the others; five data files hold signups by day, signups by channel, signup-form errors by device, the list of deploys and the newsletter sends; a folder holds four short notes, one per deploy.

• Week A: signups fell from 764 to 440. The cause was a deploy, an update pushed to the live product, on Tuesday, October 20. It added a phone field accepting digits only, which broke the form on phones.
• Week B: signups fell from 782 to 585. The cause was a weekly newsletter that was never sent; every other channel was flat.

Both folders got the same instruction:

Goal: explain why signups dropped this week (19-25 Oct 2026) compared with
last week (12-18 Oct 2026). The data is in this folder; README.md lists the
files. Start from signups by day, then follow what you find. Stop when one
cause is supported by two independent pieces of evidence, or after 12 tool
calls, in which case answer "no cause found in these sources". Report in
under 120 words: the cause or its absence, each file you used, and what you
did not check. Do not modify any file.

The agent ran twice on each folder. A workflow ran on both too: code made three fixed summaries (signups by day, by channel and by device), pasted them into one prompt, and the same model wrote an explanation with no tools.

The agent named both causes correctly and stopped well before its 12-call budget. Its first seven calls were identical in all four runs: the README, the list of files, then the five data files in the README's order. Reading all five was the sensible start whatever the data said. The model's choice showed in the folder of deploy notes: in week A it opened one of the four, the note on the October 20 deploy, and in week B it opened none and stopped. Running the same folder twice gave the same path.

That result corrects a common shortcut: identical sequences of calls do not prove the code is in charge. An agent may take the same path on different inputs when the data is small. The sign to look for is a step that only appears when the data calls for it, or a stop that comes at a different point. The workflow, by contrast, could never open the deploy notes or the newsletter sends, because its code did not fetch them; it could locate the drop and nothing more.

If you are buying a tool rather than building one, the same check works without any code. Ask for the log of one run, the list of actions the tool took with what it read and what it changed. Give it two problems with different causes, as here. If the actions and the answer come out the same both times, you are looking at a workflow, whatever the brochure says; if one run contains a step the other lacks, prompted by something it found, the model is choosing. Check too that it stops on its own with an answer such as "no cause found" instead of running until it is cut off.

A note for builders: unused connectors cost money. Connectors, or MCP servers, are small programs that plug an AI app into a service such as email or a CRM, and each adds descriptions the model rereads at every step. A first attempt, with the command line allowed and every connector installed on the machine loaded, read the README, then all five data files in one command, then the one deploy note, and reached the same diagnosis. It cost 1.36 dollars at list price, because each step carried about 310,000 tokens (the units models read and are billed in), mostly those descriptions. Each run with file tools only cost between 2 and 4 cents.

How agentic should your tool be?

The test shows a workflow locating a problem without explaining it, and often that is enough. Start from the lowest degree that does the job. Anthropic's guide recommends "finding the simplest solution possible, and only increasing complexity when needed", which "might mean not building agentic systems at all", and warns that agentic systems "often trade latency and cost for better task performance". Barry Zhang, who builds agents at Anthropic, gave a rule of thumb at the AI Engineer Summit in April 2025: if you can map out the whole decision tree easily, build that tree in code. He added that a budget of about 10 cents per task only buys 30,000 to 50,000 tokens, which points to a workflow for high-volume jobs such as customer support.

A fixed pipeline has beaten agents on their own benchmark. Agentless, a 2024 system from the University of Illinois, fixes software bugs in three fixed steps, "without letting the LLM decide future actions". It solved 32% of the SWE-bench Lite benchmark, a set of real GitHub issues, at 70 cents per issue, the best result of all the open-source software agents it was compared with, and its authors note that OpenAI adopted it to report the coding results of GPT-4o and o1.

A welcome email, a scheduled post or a stock alert needs triggers with fixed steps, as in the four triggers to build first for SaaS marketing automation. Raise the degree only when the path depends on what the system finds: debugging, research, triage of messy input. Two questions come with it. Which actions can the loop take that you cannot undo, and who approves them? How to connect an agent to your apps starts from that list. And where does each piece run, since self-hosted AI agents keep the program on your machine but rarely the model.

Gartner, which counts most "agentic" vendors as relabelled, also predicted that over 40% of agentic AI projects will be cancelled before the end of 2027, and noted that "many use cases positioned as agentic today don't require agentic implementations". Before paying for agentic, ask for one run's log and try the two-problem check above.

Agentic AI examples, and where ChatGPT fits

The word spread through product launches, each attaching it to a different noun.

The clearest case of agentic AI today is a coding agent. Asked to fix a failing test, it reads the error, opens the files it suspects, edits one, reruns the tests and repeats until they pass. Anthropic describes running its models on real software bugs with "minimal scaffolding, where the model decides which commands to run and files to edit" (February 2025).

ChatGPT is a product built on large language models. In a normal conversation it answers one message at a time, which puts it low on the scale. Its agent mode, launched in July 2025, uses "its own virtual computer" to navigate websites and run code, chooses its own steps, and "requests permission before taking actions of consequence" (OpenAI, July 17, 2025). So whether ChatGPT is an agent depends on the mode: the same app can be a chatbot or an agent. Assistants you install yourself act with the accounts you connect. OpenClaw, for example, is a free AI assistant that runs on your computer and that you message from WhatsApp, Telegram or Slack; OpenClaw alternatives compares it with the others.