What's an Agent Anyway?

Written By Livio Zanardo

ChatGPT made its research beta public in the summer of 2023, and if that sounds like a lifetime ago, you're not alone. Technology has accelerated so rapidly that making "that's so 2025" jokes already lands just right, and we're only in Q2 of 2026 as of this writing.

The breakout of OpenClaw in early 2026 brought a genuine paradigm shift. Artificial intelligence was no longer something we had to prompt in order to receive its outputs. AI applications were suddenly full-blown autonomous agents that could run workflows in minutes that would normally take a person hours or days.

The hype was real. YouTube videos titled "21 INSANE USE CASES FOR OPEN CLAW" sporting all sorts of genuinely unforeseen applications got popular fast. From simple customer-chat automation to full-blown business and financial analysis, it was all there. And the best part? It was all open source. Anyone could download OpenClaw, run it on their own computer, connect it to the LLM of their choice, and suddenly have untethered agentic capability sitting on their desk.

Too Large for Its Shell

But like any tool that arrives on the frontier of technology, the problems came too. OpenClaw would forget things, hang in the middle of a routine, or burn tokens — and cold hard cash for their usage, on seemingly simple tasks. It was a disaster, but that's to be expected from an open-source project that became the fastest-growing open-source project in history in a matter of weeks, as the CEO of NVIDIA, Jensen Huang, put it.

And it turned out OpenClaw wasn't unique in its growing pains, it was just the most visible. Across the industry, the same lesson was surfacing: these systems are powerful, but they are not "set it and forget it." AI still hallucinates at meaningful, task-dependent rates — commonly around one in five, and a 2026 paper memorably called "The Reasoning Trap" found that training a model to reason better can make it hallucinate more when reaching for tools, not less. The fixes exist, retrieval, verification, guardrails, and they cut errors dramatically, but they all point in the same direction: agents need checking. Even the money surprised people. As pay-per-use pricing took over, 78% of IT leaders reported unexpected charges in a single year. The capability was real. So were the limits.

This was explosive growth at a rate open source had never seen. The creator of OpenClaw was acqui-hired by OpenAI within weeks of its spread. Mac Minis are still on backorder as hobbyists and technologists hunt for a separate machine to run it on without bogging down their main one. NVIDIA eventually shipped its own enterprise-grade wrapper and family of models. It was the fastest any single idea had gone from a GitHub repo to the center of the technology conversation, and it happened with no company, no launch, and no marketing budget behind it.

OpenClaw wasn't the only agentic framework around, but it was the first to make "continuous, 24/7 agents" feel real, software that behaved like a digital coworker, with self-learning capabilities and no human babysitting required. And the best part? We are all still early.

Agent = Model + Harness

So, what's an agent anyway?

An agent, in software terms, is an autonomous entity that can execute tasks, carry out functions, or make decisions on behalf of a person, business, or institution. It lets you delegate the work you'd rather not do, provided you give it the right tooling, context, and instructions. The way this actually works is almost anticlimactic: you take an AI model, which at its core is just a very fast data-processing engine, and you hand it a set of digital tools plus instructions on how to use them.

The cleanest way to tell an agent apart from the chatbot you're already used to: a chatbot answers; an agent acts. One replies to your question. The other qualifies the lead, books the meeting, runs the research, and finishes the multi-step task, then tells you it's done.

Here's a distinction worth holding onto: OpenClaw is not an agent in itself. It's an environment, a set of prebuilt tools that runs agent routines internally. The agents are the individual programs and routines it deploys, and several may be working inside it at once. What makes them work is their connection to the same AI models we're all accustomed to prompting, except that instead of being prompted by a human, they're prompted by a program, with a set of instructions and a toolbox, free to decide for themselves which tool to reach for.

And that's why an agent is not the same thing as the "general-purpose technology" that ChatGPT or Claude represent. Those are natural-language processing tools: text in, text out, based on the statistical models baked into the software. An agent is that same model given awareness and a set of tools, pointed at the harder problems, decision-making, problem-solving, moving information between systems, dispatching real-world services.

The Silicon Employee

This is the part that matters, why it stopped being a curiosity and why it's impossible to have a conversation about the technology industry without an "Agentic" mention. Once a model can act, it stops being a clever toy and starts being a line item. The market reflects it: AI agents were a roughly $11 billion market in 2026, with credible projections putting it past $50 billion by the end of the decade. More than half of small businesses are now at least piloting them, and the businesses using them aren't chasing novelty, they're chasing hours. A modest stack of agents, at $20 to $70 a month each, is reported to free up twenty to thirty-plus hours a week, and the overwhelming majority of small businesses running AI say it moved revenue, usually by answering a lead faster than a competitor could.

You can see it in the names you'd recognize. Salesforce's agent product crossed $800 million in annual revenue. Whole agencies now run on it, deploying agents for clients across many countries. Even research, the slow, expensive, human part, has its own agents now: one of them just raised $350 million at a $7.5 billion valuation. None of that is a demo anymore.

So why now, and not in 2023? Mostly plumbing. A quiet standard called MCP became the "USB-C port" for AI, the universal way an agent plugs into your tools and your data without a custom integration for each one. That's the unglamorous breakthrough that turned agents from impressive demos into things that actually touch your stack. The rest is the open-source flywheel OpenClaw kicked into motion: a library of reusable agent skills that crossed well over a million entries and keeps growing every week.

The Honest Version

The rise of AI has come with real job displacement — mass, visible, and still unfolding. And the "employee" framing doesn't soften it: if an agent is a coworker, the fear goes, it's also a replacement. The hype was real; so is that fear. But it mistakes what kind of coworker this is. A worker that can only repeat the past is not the one who decides what the company does next.

Because that's what a model is. It's trained on historical data — nearly everything already written, built, and tried — and that's exactly where its precision comes from: it has seen the pattern before, so it runs the mechanical and the already-charted with a consistency no human matches. The same thing that makes it sharp makes it blind. It cannot predict the future, and it cannot build one. It only knows what has already happened.

The only people who can predict the future are the ones building it — the founders. And here's the part the panic skips: the displacement is itself a human decision. So is every dollar that moves into a frontier industry. An agent doesn't choose where the world goes next, or stake a company on it; people do, and then point the agents at the parts that never needed a human to begin with.

So the honest version isn't replacement. It's refinement. Agents make it cheap to deliver the work that doesn't require nuance — and, directed by someone who knows what "good" looks like, to build the nuanced work better than either side could alone. The intuition, the judgment, the leap into territory the data has never seen: that's the part that still needs you, and always will. (What becomes possible once the rote is handled and you're freed to get radically specific — that's the next story.)

And we're all still early — but early isn't the same as untouched. Soon enough you won't be reading about agents. You'll be working alongside one. In fact you probably already are: the lead that got qualified before you woke up, the meeting that booked itself, the research waiting in your inbox by morning. You just didn't know to call it one. But notice what it took off your plate — none of it was ever the work that was truly yours. That part, the part only you can find, is still waiting.

Article written by Livio Zanardo
Contributing Writer | Fort Lauderdale Tech Meetup

Livio Zanardo is a multidisciplinary technologist and creative leader. He’s a fractional CTO with a people-first, impact-driven approach, working with clients like the Miami Foundation, Winning Hands App, and Cortada Foundation.

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Sources

Sources, re-verified against primary sources (2026-06-16; the underlying leads came from the OpenLegatus weekly brief of 2026-06-08):

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