AGI vs Agentic AI: What's the Difference?
One is a product with a price per action. The other is a threshold the labs define differently, and the contract that once hinged on it has been rewritten.
- Different questions. Agentic AI describes how a system works: it pursues a goal over several steps with tools. AGI describes how capable a system is across nearly all work.
- One exists, one does not. Agentic AI is sold today by the conversation, the action or the resolved case. No system has been verified as AGI.
- Two dials, not one. Google DeepMind rates autonomy and capability on separate scales, and says fully autonomous agents likely need its fourth capability level, Exceptional AGI.
AGI vs agentic AI compares two different things. Agentic AI is about behavior: a system that takes a goal, plans the steps, calls tools and acts until the job is done. AGI, artificial general intelligence, is about breadth: a system as capable as skilled people across most work. You can buy the first this quarter. The second has no agreed definition and no verified example.
AGI vs agentic AI at a glance
AGI vs agentic AI side by side shows how little the two terms overlap.
| Question | Agentic AI | AGI |
|---|---|---|
| What it describes | How a system acts: goal, plan, tools, steps | How capable a system is across most tasks |
| Exists today? | Yes, sold by Salesforce, Microsoft, HubSpot and others | No system has been verified as AGI |
| Who defines it | Engineering guides from OpenAI and Anthropic | Each lab, in its own words |
| How it is measured | Task success, task length, cost per outcome | Breadth and depth of performance against skilled adults |
| How you pay | Per conversation, per action or per resolved case | Not for sale |
| The buyer's risk | Errors inside a multistep task, a usage bill that runs | Pitches and contract terms tied to an undefined label |
What agentic AI is
Agentic AI is software that pursues a goal over several steps instead of answering one prompt. OpenAI's practical guide to building agents puts it in one line: agents "independently accomplish tasks on your behalf." The same guide says chatbots and single-turn model apps do not count. Anthropic's Building effective agents (December 2024) draws a finer line: workflows run models through predefined code paths, while agents let the model direct its own process and tool use.
None of that requires general intelligence. A support agent that looks up an order, checks the refund policy and issues the refund is agentic, and it can be excellent at that one job while useless at the next. That narrowness is what lets vendors price agent work by the unit, the model we track as Agentic AI as a Service (GaaS).
What AGI is
AGI is a level of capability, not a feature you switch on. The OpenAI Charter defines it as "highly autonomous systems that outperform humans at most economically valuable work." Google DeepMind's Levels of AGI paper (first posted November 2023, revised September 2025) splits the idea into rungs of performance and generality: Emerging, Competent, Expert, Exceptional and Superhuman. Its table puts chatbots such as ChatGPT at Emerging AGI, the first rung, and the paper says OpenAI's labor-replacement threshold better matches Exceptional AGI, the fourth.
Not every lab uses the term. Anthropic's chief executive, Dario Amodei, called AGI "an imprecise term" in his October 2024 essay Machines of Loving Grace and wrote about "powerful AI" instead. The definitions disagree on where the line sits, and none comes with a test that would settle whether a system has crossed it.
How AGI and agentic AI relate
AGI and agentic AI relate as two separate dials, and the DeepMind paper says so directly. It rates autonomy on its own scale, from Level 1, AI as a Tool, to Level 5, AI as an Agent, which it defines as "fully autonomous AI." Its example for Level 5 is autonomous personal assistants, marked "not yet unlocked," and it says that level likely needs Exceptional AGI underneath.
Today's commercial agents turn the autonomy dial partway up inside narrow jobs, with a person approving the risky steps. AGI is about the other dial, generality. OpenAI's internal five-level scale, reported in July 2024 and never published by the company, tells the same story: Chatbots, Reasoners, Agents, Innovators, Organizations. On that ladder, agents are the third step toward AGI, not the destination.
How far today's agents are from AGI
The distance between today's agents and AGI shows up in how long a task an agent can finish on its own. METR's March 2025 study found that the length of task frontier models complete with 50% success had doubled about every seven months over six years. On METR's time-horizon tracker, GPT-5 measured about 2 hours and 17 minutes. In May 2026 METR put Claude Mythos Preview at 16 hours or more, a result it said sits at the edge of what its task suite can measure, The Decoder reported.
That curve is steep, and the measure is still a coin flip on one long task. A 50% success rate is workable when a person reviews the output. It is not the reliability a business needs to hand over a job unchecked, and it says nothing about breadth across all the work a company does.
The market is pricing that gap. Gartner predicted in June 2025 that over 40% of agentic AI projects will be canceled by the end of 2027, and cited rising costs, unclear business value and weak risk controls.
Why the AGI label matters to buyers
The AGI label has been a contract term, not just a research goal. Microsoft wrote on October 28, 2025 that once OpenAI declares AGI, "that declaration will now be verified by an independent expert panel," with Microsoft's IP rights running through 2032. In April 2026 the two companies removed the AGI clause, The Verge reported. The expert panel is gone, and OpenAI's revenue-share payments now end on a schedule "independent of OpenAI's technology progress." A term that once moved billions of dollars no longer moves any.
For a buyer the lesson is smaller and more useful. An agent contract should name the task, how success is counted and the price per completed unit. If a pitch leans on "AGI" or a "general" agent, ask for the task list, the success rate on your own data and what you pay when the agent fails. The multi-agent systems sold today are built from narrow agents, and they are priced that way.
Which one to plan for
Plan for agentic AI, because it ships now, bills now and fails in ways you can measure. Treat AGI as a forecast. Forecasts move, and the labs that make them also sell the agents. For how agents differ from the chat tools most teams started with, see agentic AI vs generative AI.
Frequently asked questions
Is agentic AI the same as AGI?
No. Agentic AI describes how a system works: it takes a goal, plans steps, uses tools and acts. AGI describes how capable a system is across nearly all work. A narrow agent that only resolves refund cases is agentic and nowhere near general.
Is agentic AI a step toward AGI?
OpenAI treats it as one. Its internal five-level scale, reported in July 2024, put Agents at level 3, ahead of Chatbots and Reasoners and behind Innovators and Organizations. Google DeepMind's Levels of AGI paper says fully autonomous agents likely need Exceptional AGI, its fourth capability level.
Has anyone reached AGI?
No system has been independently verified as AGI. Microsoft's October 2025 terms required an independent expert panel to verify any AGI declaration by OpenAI, but the April 2026 deal removed the AGI clause and the panel with it, The Verge reported.
What is the difference between AGI, agentic AI and generative AI?
Generative AI produces content in response to a prompt. Agentic AI uses generative models to pursue a goal over several steps with tools. AGI is a level of capability across most tasks that no system has been shown to reach.
Can you buy AGI?
No. You can buy agentic AI today, priced per conversation, per action or per resolved case. Any product sold as AGI should be read as marketing until the vendor shows task-level results on your own work.
- OpenAI Charter
- Google DeepMind, Levels of AGI
- OpenAI, A practical guide to building agents
- Anthropic, Building effective agents
- Dario Amodei, Machines of Loving Grace
- METR, task length study
- METR, time horizons
- Gartner, agentic AI project forecast
- Microsoft, OpenAI partnership terms
- The Verge, contract renegotiation
- Axios, OpenAI five levels
