Everyone's talking about AI agents. Almost no one can explain what they actually are in terms that matter to a business buyer. Here's the explanation I wish existed when I started working on this.
AI agents are software systems that can perceive their environment, make decisions, and take actions to achieve goals — without a human directing each step. The key distinction from traditional AI: agents act, not just respond. For enterprise leaders, the practical implication is that agents can handle multi-step business processes end-to-end, including handling exceptions and escalating to humans when needed. The limiting factor isn't the AI — it's whether your processes and infrastructure are built to support autonomous execution.
Three years ago, if you asked a room of enterprise technology leaders what an "AI agent" was, you'd get blank stares. Today, every major software vendor has an "agentic" product. Every analyst firm has an "AI agent framework." Every CIO has an AI agent strategy — or at least claims to.
And yet, when I ask enterprise buyers to explain what an AI agent actually does, I usually get one of two answers. The first is a vague gesture toward "autonomy" and "intelligence." The second is a very specific technical description that doesn't connect to any business outcome.
Neither answer helps. So here's mine.
An AI agent is a software system that can perceive its environment, decide what to do, and act on that decision — in a loop — without a human directing every step.
That's it. Perception, decision, action. In a loop.
The loop is the critical part. Traditional AI tells you something. An agent does something with what it knows, then perceives the result of that action, then decides what to do next. It keeps going until the goal is achieved — or until it needs human input.
The marketing of AI agents has leaned heavily on the word "autonomous." This is a mistake — not because the technology isn't impressive, but because autonomy without qualification terrifies enterprise buyers.
What enterprise leaders actually want is directed autonomy. They want an agent that can handle the 80% of a process that's routine and well-defined, and escalate the 20% that requires human judgment. They want an agent that leaves an audit trail. They want an agent they can override.
"The governance-first companies will out-execute the move-fast companies within 18 months. Compliance infrastructure isn't a constraint on AI deployment — it's the foundation that lets you scale faster."
This is the positioning insight that took me a long time to internalize: governance is the feature, not the limitation. The enterprises that are building human-in-the-loop controls into their AI agents from the start are going to be the ones that scale without the constant risk reviews that are killing AI programs at less disciplined competitors.
If you've been confused about the difference between an AI assistant (like ChatGPT) and an AI agent, you're not alone. Here's the practical distinction:
1. Chatbots respond. Agents act. A chatbot answers your question. An agent takes the action that your question implies — and keeps taking actions until the job is done.
2. Chatbots are stateless. Agents have memory. Every chatbot conversation starts fresh. Agents remember what they've done, what they've learned, and where they are in a multi-step process. This is why "durable execution" is such an important concept in agentic AI — the agent needs to persist state across sessions, system restarts, and human approval steps.
3. Chatbots use one tool. Agents orchestrate many. A chatbot queries a language model. An agent can query a language model, call an API, update a database, send an email, trigger a workflow, and wait for a human response — all within a single task.
Here's the question I'd ask if I were a CIO evaluating an agentic AI investment: not "how intelligent is the agent?" but "how well does this platform handle failure?"
Because the production reality of multi-agent systems is that failure is the default. Agents fail silently. They hand off corrupted state. They loop indefinitely without a durable execution layer underneath them.
The builders shipping real agentic applications aren't the ones with the best language models — they're the ones who've built the most robust exception handling, audit trails, and human escalation paths.
The intelligence layer is nearly commoditized. The execution layer — the nervous system that connects AI intelligence to real business processes — is where the durable competitive advantage lives.
That's the question worth asking your vendors. Not "how smart is your AI?" but "what happens when it fails?"
Kuber Sharma is Sr. Director, Product Marketing at UiPath, leading GTM for Autopilot — UiPath's flagship agentic automation product. Previously led PMM for Tableau AI and Salesforce Einstein at Salesforce, and Azure AI at Microsoft. He writes the Positioned newsletter on Substack.
Kuber Sharma leads platform product marketing at UiPath. Previously led PMM for Tableau AI and Salesforce Data Cloud at Salesforce, and for Azure AI and Azure Synapse at Microsoft. He writes Positioned, a newsletter on AI-era product marketing strategy.