Every week, I talk to founders and operators who want to 'build an AI agent.' When I ask what they mean, the answer usually describes a chatbot—something that answers questions based on documents or handles basic customer queries.
That's fine. Chatbots are useful. But they're not agents, and the distinction matters more than most people realize.
What makes an agent different
A chatbot responds to inputs. An agent takes actions. That sounds simple, but the implications are profound.
When you ask a chatbot 'What's our refund policy?', it searches your docs and returns an answer. When you tell an agent 'Process this refund,' it checks the order status, verifies the customer's eligibility, initiates the refund in your payment system, updates the order record, and sends a confirmation email.
The chatbot gives information. The agent does work.
Why this matters for ROI
Here's where it gets interesting from a business perspective. Chatbots reduce time spent answering questions. That's valuable, but the ceiling is limited—you're optimizing an existing process.
Agents eliminate processes entirely. Instead of making a human faster at processing refunds, an agent processes refunds without human involvement at all. The ROI calculation is completely different.
When we work with customers, we push them to think beyond 'answering questions faster' to 'what work could disappear entirely?' That's where the real value lives.
The technical requirements are different too
Building a chatbot is relatively straightforward: embed your documents, add retrieval, connect to an LLM, done. You can ship something useful in a week.
Building an agent requires systems thinking. You need to handle authentication across multiple services, manage state across multi-step workflows, implement proper error handling and rollback logic, design for partial failures, and build monitoring that catches issues before they become problems.
This is why so many 'agent' projects stall after the demo. The demo works because everything goes right. Production requires handling everything that goes wrong.
How to know which you need
Ask yourself: Is the goal to help humans do work faster, or to do work without humans?
If you want to help your support team answer questions more quickly, build a chatbot. It's simpler, cheaper, and you'll get value immediately.
If you want to automatically categorize expenses, process insurance claims, or qualify leads without human involvement, you need an agent. It's more complex, but the payoff is larger.
Most companies need both, deployed thoughtfully in different parts of their operations.
The path forward
We're still early in understanding where agents make sense versus chatbots. The technology is evolving fast, and the best practices are still emerging.
What I can say with confidence: the companies seeing the biggest returns are the ones thinking clearly about this distinction from the start. They're not just asking 'where can we use AI?' but 'what work could we eliminate entirely?'
That's a harder question, but it's the right one.