The agentic AI revolution isn’t the future, it’s already here

Summary
- OpenAI’s o3 and o4 agents mark an inflection point in the journey of artificial intelligence (AI) taking over human tasks. AI has evolved from perceptual and generative functions to agentic work.
Jensen Huang unveiled a simple exponential curve at the Consumer Electronics Show in January, where he talked about how AI has moved from Perception AI (machine learning, speech recognition, etc) to Generative AI (with ChatGPT and other models), and how AI will then become Agentic (a coding and personal assistant), before moving to Physical AI with robots and self-driving cars.
The last two years have seen startling advances in GenAI, but the phrase ‘AI Agent’ has stayed mostly on whiteboards for years, invoking a future in which AI does not merely tell you what to do, but goes out into the wide world to do it. In technology, as in life, things happen gradually and then suddenly. The ‘sudden’ moment for AI agents seems to have come this week, as the future tipped into the present, headlined by the release of the confusingly named o3 and o4-mini by ChatGPT-maker OpenAI. Many of us, however, are yet to notice this tipping point.
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But first, what exactly is an AI agent? In classic computer-science terms, an agent is a program that perceives its environment, decides what to do next and then acts towards a goal—iteratively, autonomously and with minimal human micro-management. Chatbots, by contrast, are largely reactive: they return text when prompted, but rarely take the next step. Agents blur that boundary, wielding tools such as browsers, code, CRM dashboards or payment interfaces to complete multistep tasks that once required a person’s cursor and keyboard. So, a chatbot will help plan your perfect holiday itinerary; an AI Agent will also do the bookings for you.
Even before this month, there were proto agents out there. Deep research by almost every large player from OpenAI to Google could compile sources or summarize regulations to produce a research report on almost anything. OpenAI’s browser agent Operator took this further, piloting its own mini-browser to read, scroll and fill forms online. Adept’s ACT1 learnt to operate enterprise SaaS apps; and so on.
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But, arguably, the biggest leap came when OpenAI unveiled o3 and its big brother o4. These were not mere models, but as OpenAI President Greg Brockman said, “…they’re really AI systems." That distinction matters. A model is the neural network brain that predicts the next token; a system wraps that brain in orchestration layers—tool routers, memory stores, validators, even secondary models—that monitor progress and decide which tool (or subskill) to invoke next.
In other words, o3/o4 are agentic by design: they can write code, run it, open a browser to verify an answer and loop until a goal is satisfied, all behind a deceptively simple chat interface. For end users, it still feels like chatting; under the hood, the system runs through a checklist the way an assistant or intern might. OpenAI is not alone. Google’s Gemini is folding agent capabilities into Workspace; Microsoft’s Copilot is adding ‘autocomplete tasks’ for Excel and Teams; and Salesforce is training AI representatives that log calls automatically.
It is agents that will finally bring GenAI to enterprises. Finance teams are trying out agents that monitor cash flow anomalies at 3am and file ledger fixes before humans log in. E-commerce firms are letting agents test storefront copy and take the better one live. In manufacturing, prototype maintenance agents read sensor data, cross-check manuals and schedule down time. Professional services giants are piloting ‘first draft’ research partners that read contracts, assemble evidence and even initiate further information requests. Agents will transform service-as-a-software, with users paying for services delivered by orchestrated agents rather than for access to software.
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Agents raise ethical questions. There is something deeply uncomfortable about giving human agency to a machine. If models hallucinate, multi-agent frameworks will hallucinate in a compounded way, resulting in a high probability of task failure.
Above all, agents are a direct threat to human jobs. Sam Altman predicts agents “will materially change the output of companies," while Bill Gates calls them a “shock wave" that turns apps into proactive assistants. Huang predicts that “IT will become the HR of AI agents" as the CIO decides which agents to bring into the workplace to augment or replace human resources.
As I conclude the last of my Tech Whisperer columns for Mint, here’s a thought: The companies that figure out how—and how far—to delegate work to agent colleagues will set the pace for the rest of the decade. Everyone else will be playing catch-up.
The author is a founder of AI&Beyond and the author of ‘The Tech Whisperer’.