Tutorials Agentic AI with .NET Tutorial
AI Agents vs Chatbots
AI Agents vs Chatbots: free step-by-step lesson with examples, common mistakes, and interview tips — part of Agentic AI with .NET Tutorial on Toolliyo Academy.
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Agentic AI with .NET Tutorial · Lesson 2 of 120
AI Agents vs Chatbots
Foundations & SK → Tools & RAG → Product & Ops → Projects
Foundations & SK · 1 — Agent basics · ~6 min · Agentic Foundations
What is this?
A chatbot maps user text to a reply. An agent can call tools, keep state, and continue until a success condition is met.
Why should you care?
If you only need FAQ answers, a chatbot is enough. If you need “check inventory then create a draft PO”, you need an agent.
See it live — copy this example
Use .NET 8+. Run Semantic Kernel samples in a console or Web API. Keep API keys in user secrets or environment variables — never in source.
public interface IAgentStep
{
Task<AgentObservation> ActAsync(AgentState state, CancellationToken ct);
}
// Chatbot: one CompleteAsync(prompt)
// Agent: while (!state.Done) await step.ActAsync(state, ct);
What happened?
- IAgentStep is a tiny contract for one action.
- The loop is what makes behavior agentic.
Practice next
- Draw chatbot vs agent on paper.
- build a fake step that only logs.
- Add a Done flag after 2 steps.
- Add a timeout CancellationToken.
- Return a user-visible progress message each step.
Remember
Chat = one shot; agent = loop. State carries memory and tool results. Stop conditions prevent runaway cost.
Order status helper
User asks “where is my order?”
Outcome: Agent calls Orders API; chatbot would only guess.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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