We are now entering the third wave of generative AI – one that promises not just conversation, but autonomy. From the first chatbots to today’s AI assistants, technology has steadily evolved. Now, the arrival of AI agents marks a new era: intelligent systems that don’t just follow instructions but pursue goals, make decisions, and use digital tools to act on their own.

At the forefront of this shift is OpenAI’s new ChatGPT agent. Built by merging two existing systems – Operator and Deep Research – this upgraded agent is designed to both “think and act,” blurring the line between passive software and active digital worker.

Unlike chatbots that merely respond, or assistants that follow human-led tasks, agents operate with a degree of autonomy. They can work in teams, delegate tasks among themselves, and even access tools like browsers, spreadsheets, or payment systems to complete objectives. In theory, they can coordinate, plan, and solve problems far more efficiently than a single tool – or even a single human.

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This leap hasn’t come from nowhere. Agentic AI has been accelerating rapidly. One major breakthrough came last October, when Anthropic upgraded its Claude chatbot to interact with a computer like a human. Suddenly, agents could search databases, gather and analyse information, and submit forms – all without direct human input.

OpenAI, Google, Microsoft, Meta and several Chinese startups quickly joined the race. Startups like Monica demonstrated AI agents capable of purchasing real estate and summarising lecture recordings. Another, Genspark, built an AI that returns search results as task-ready pages, complete with links to perform actions. Not all projects succeed – one company’s chaotic “cheat at anything” agent made headlines for its bizarre behaviour but underwhelming output.

Still, the potential is clear. In some sectors, such as coding and software engineering, agents are already rewriting the rules. Microsoft’s Copilot and OpenAI’s Codex can write, test, and commit code autonomously – as well as diagnose errors in human-written code. What once took hours or days can now be done in moments.

But the real superpower of AI agents lies in search and summarisation. Where a human researcher might spend days sifting through data, AI agents can complete the task in minutes. OpenAI’s Deep Research does this with multi-step analysis across the internet. Google’s “co-scientist” aims even higher – assisting real-world researchers in generating new scientific hypotheses.

Yet, these capabilities come with serious warnings. Despite their sophistication, AI agents remain prone to error. Both OpenAI and Anthropic advise that agents should be closely supervised to prevent mistakes. One agent famously went off the rails when tasked with managing a vending machine, stocking it with tungsten cubes instead of snacks. Another mistakenly deleted an entire database and later “admitted” it had panicked.

Despite these failures, agents are already being integrated into real-world businesses. Telstra began deploying Microsoft Copilot across the company in 2024, with staff reportedly saving one to two hours per week on administrative tasks. Smaller firms, too, are experimenting. In Canberra, a construction company is using an AI agent to monitor and manage building defects in its apartments.

However, this rapid adoption raises serious questions. Chief among them is the risk of job displacement. As AI agents grow more capable, entry-level white-collar jobs could be the first to disappear. And even for those still employed, relying too heavily on AI could reduce human skill and oversight. If left unchecked, a single flawed decision by an agent could snowball into serious real-world harm.

There's also the issue of energy use. Generative AI is notoriously resource-intensive. As agents take on more complex tasks, the environmental and financial cost of using them may rise sharply.

Even so, the momentum is undeniable. AI agents are evolving fast, and their presence is already being felt across industries and professions. For most people, they’ll become part of daily life whether by choice or necessity. Tools like Microsoft Copilot Studio now make agents accessible to the average user, while frameworks like Langchain let developers build custom agents with just a few lines of code.

The future of work, productivity, and even thinking is being reshaped right now – not by humans alone, but by AI agents with goals of their own. Whether they become our most powerful partners or our most unpredictable liabilities remains to be seen.

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