From models to autonomous agents

Over the past few years, AI has shifted from an abstract concept to something we use every day. Why? Ease of use — you ask a model a question, it gives you an answer. Simple. 

But as powerful as these models are, this simplicity results in limitations. One of the biggest being that it’s a one-shot exchange. AI models are mostly reactive; they do what we ask, and when we stop interacting, they stop responding. 

AI agents, on the other hand, are designed to take initiative. Instead of waiting for your next prompt, they plan, act, and adapt to achieve a goal. A good way to visualize this is three distinct layers of intelligence:

1. AI Models: These are the foundation. They’re systems trained to understand and generate text, images, or data.

2. AI Tools: These separate models into specific applications, like summarizers, translators, or chat assistants.

3. AI Agents: These sit at the top of the stack. They’re systems that can reason about tasks, break them into steps, use tools autonomously, and decide what to do next.

In other words, while a model answers questions, an agent figures out what questions to ask next. That distinction changes everything — it’s the difference between a calculator and a personal assistant.

How AI Agents actually work

At their core, AI agents combine language models, tool use, and feedback loops to operate semi-autonomously. 

Let’s say you give the agent a high-level instruction like “book a flight to London next week.” Instead of producing a single answer, the agent interprets your request as a goal — something to be achieved, not just answered. 

So, whereas an AI model might sort through flight websites and find the cheapest, shortest, or best options, an agent decides how to reach that goal. This planning process might involve reasoning about multiple steps, like: 

• Searching for flights

• Comparing prices

• Checking your calendar

• Sending a confirmation email

Each step becomes a task the agent can execute or delegate to another system. The agent can use external tools or APIs, like browsing the web, querying databases, or running code. This is what allows them to interact with the real world, not just generate text.

AI agents also learn on the go through feedback loops. Your agent might offer a few flight options to check whether they’re on the right track and correct themselves. But some agents simply track their own reasoning chains to improve decision-making next time.

And they don’t always work alone. Multi-agent systems, where specialized agents communicate and collaborate, are becoming more common. Think of one agent gathering flight research, another analyzing it, and a third summarizing the findings for your review. This kind of division of labor makes AI workflows more scalable and mirrors how human teams operate.

Why AI Agents are everywhere right now (and what’s next)

The sudden explosion of “agentic” tools isn’t just hype. It’s the result of several converging trends in AI development, including:

• Better foundation models: Modern LLMs are getting better at reasoning, following instructions, and maintaining context over long sessions — with the help of AI trainers! 

• The rise of open frameworks: Specialized projects have made it easy for developers to build custom agents that can plan, call APIs, and interact with each other.

• Tool use integration: Agents can now plug into tools like browsers, code interpreters, CRMs — you name it. This makes them genuinely useful, not just conversational.

• Automation, everywhere: From startups to enterprises, everyone wants to reduce repetitive tasks. Agents deliver a middle ground between human-level judgment and machine-level efficiency.

Right now, most AI agents are specialists. They do really well with specific tasks like drafting reports, summarizing meetings, and handling support tickets. But the real transformation will come as agents evolve into ecosystems that interact with humans, each other, and the digital world. 

Here are a few big ideas and topics we think everyone will be talking about in the (very near) future:

• Personalization and memory improvements will create an almost human-like assistant:

Today’s agents often start from scratch with each session. The next generation will remember your preferences, style, and history.

• Agent swarms will take care of the small stuff so experts can focus on the big stuff: 

Imagine dozens of agents working together on complex problems. These coordinated systems could potentially handle everything from product development strategies to drug discovery research.

• AI governance and trust will become an even bigger hot topic: 

As agents gain autonomy, control and oversight will become critical. How do we make sure they act within constraints, use data responsibly, and make their reasoning transparent? 

• Human-AI collaboration will become second nature: 

The ultimate goal: create workflows where humans and agents work in tandem. Humans define the goals, while agents handle the heavy lifting.

We’re entering a stage where interacting with AI is quickly evolving from question-answer conversations to orchestrating a team of digital collaborators that can think, act, and learn alongside us.