Table of Contents

What Is the Difference Between an AI Agent and a Chatbot?


TL;DR

A chatbot responds to user messages in a conversation. An AI agent reasons about goals, makes plans, and takes actions using tools. In LM-Kit.NET, MultiTurnConversation is the chatbot: simple, stateful, conversation-focused. Agent is the autonomous worker: it plans, calls tools, delegates to other agents, and iterates until a task is complete.


Side-by-Side Comparison

Dimension Chatbot (MultiTurnConversation) Agent (Agent)
Purpose Sustained multi-turn conversation Goal-oriented task execution
Interaction User sends message, model replies User defines a task, agent works autonomously
Planning No planning. Responds turn by turn. Planning strategies: ReAct, Chain-of-Thought, Plan-and-Execute, Reflection
Tools Optional. Simple per-turn tool policy. Full tool registry with permission policies and approval workflows.
Iterations One response per user message Multiple reasoning/action cycles per task
Memory Conversation history (automatic) Agent memory with long-term knowledge across sessions
Delegation N/A Can delegate subtasks to other agents
Grammar constraints Supported (structured output) Not directly (uses tool output for structure)
Best for Customer support chat, Q&A, interactive assistants Research tasks, document processing, multi-step workflows, automation

When to Use a Chatbot

Use MultiTurnConversation when the interaction is conversational: the user sends messages, the model responds, and the conversation continues. This is the right choice for:

  • Customer support chat: Answer questions, maintain context across turns.
  • Interactive Q&A: Users ask questions and get direct answers.
  • Simple tool use: One tool call per turn (e.g., calculator, date lookup).
  • Structured output: Force responses into JSON, lists, or other formats using grammar constraints.
using LMKit.Model;
using LMKit.TextGeneration;

using LM model = LM.LoadFromModelID("qwen3.5:9b");
var chat = new MultiTurnConversation(model);
chat.SystemPrompt = "You are a helpful customer support agent for Acme Corp.";

// Simple conversational loop
string answer = chat.Submit("What is your return policy?");
string followUp = chat.Submit("Does that apply to electronics?");

When to Use an Agent

Use Agent when the task requires autonomous reasoning: the agent needs to think about the problem, decide what actions to take, execute them, observe the results, and iterate. This is the right choice for:

  • Research tasks: Search the web, read results, synthesize findings.
  • Document processing: Analyze PDFs, extract data, validate results.
  • Multi-step workflows: Plan a sequence of actions and execute them.
  • Tool-heavy automation: Call multiple tools, handle errors, retry with different strategies.
  • Multi-agent systems: Break complex tasks into subtasks handled by specialized agents.
using LMKit.Model;
using LMKit.Agents;
using LMKit.Agents.Tools.BuiltIn;

using LM model = LM.LoadFromModelID("qwen3.5:9b");

var agent = Agent.CreateBuilder(model)
    .WithPersona("Research Assistant")
    .WithPlanning(PlanningStrategy.ReAct)
    .WithTools(tools =>
    {
        tools.Register(BuiltInTools.WebSearch);
        tools.Register(BuiltInTools.Calculator);
        tools.AddFileSystemTools();
    })
    .WithMaxIterations(10)
    .Build();

// The agent plans, searches, reasons, and produces a final answer
var result = await agent.RunAsync("Compare the latest quarterly revenue of Apple and Microsoft.");

Planning Strategies Explained

Agents in LM-Kit.NET support multiple planning strategies that control how they reason:

Strategy How It Works Best For
None No planning. Single-shot response. Simple tool-calling tasks.
Chain-of-Thought Thinks step by step before answering. Reasoning and math problems.
ReAct Cycles through Thought, Action, Observation until done. Research, web search, multi-tool tasks.
Plan-and-Execute Creates a full plan first, then executes each step. Complex multi-step workflows.
Reflection Generates an answer, then critiques and improves it. High-quality writing and analysis.

Can I Combine Both?

Yes. A common pattern is to use an Agent for complex task execution and a MultiTurnConversation for interactive follow-up:

  1. User describes a complex task.
  2. An Agent executes it autonomously (research, processing, extraction).
  3. Results are presented in a MultiTurnConversation where the user can ask follow-up questions about the results.

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