AI agents are becoming popular in modern applications because they can understand user requests, make decisions, use tools, and complete tasks automatically.
In this tutorial, we will build a simple AI agent concept using Kotlin and understand the basic architecture behind AI-powered applications.
What is an AI Agent?
An AI agent is a system that can:
- Understand user input
- Reason about the task
- Use external tools or APIs
- Remember context
- Return a useful response
A simple chatbot only answers questions:
User → AI → Response
An AI agent can take actions:
User
↓
AI Agent
↓
Reasoning
↓
Tools / APIs / Database
↓
Final Response
Examples:
- Personal assistants
- Coding assistants
- Customer support bots
- Automated workflows
AI Agent Architecture
A simple AI agent contains these components:
AI Agent
├── LLM (Brain)
├── Memory
├── Tools
└── Agent Logic
1. LLM (Large Language Model)
The LLM understands language and generates responses.
Examples:
- OpenAI models
- Gemini
- Claude
- Local LLMs
2. Memory
Memory allows the agent to remember previous interactions.
Example:
User: My name is Alex
Agent remembers:
User name = Alex
3. Tools
Tools allow the AI agent to perform actions.
Examples:
- Search API
- Weather API
- Database queries
- File operations
Creating a Simple AI Agent in Kotlin
For Kotlin applications, we can use HTTP clients to communicate with an AI model API.
Add dependency:
implementation("io.ktor:ktor-client-core:2.3.7")
implementation("io.ktor:ktor-client-cio:2.3.7")
Create an AI Service
class AIService {
private val client = HttpClient(CIO)
suspend fun askAI(
prompt: String
): String {
// Call your AI API here
return "AI response"
}
}
This service is responsible for communicating with the AI model.
Create an Agent Class
The agent controls the workflow.
class AIAgent(
private val aiService: AIService
) {
suspend fun execute(
task: String
): String {
val prompt = """
You are an AI assistant.
Complete this task:
$task
""".trimIndent()
return aiService.askAI(prompt)
}
}
Now our agent can receive tasks and ask the AI model for solutions.
Adding Simple Memory
An agent can store previous conversations.
class Memory {
private val history =
mutableListOf<String>()
fun add(message:String){
history.add(message)
}
fun getHistory():List<String>{
return history
}
}
Usage:
val memory = Memory()
memory.add(
"User likes Kotlin"
)
println(memory.getHistory())
Adding Tools
Tools allow agents to interact with external systems.
Example:
interface Tool {
suspend fun execute(
input:String
):String
}
Weather tool:
class WeatherTool:Tool {
override suspend fun execute(
input:String
):String {
return "Temperature is 20°C"
}
}
The agent can now call tools when required.
Agent Workflow Example
User:
What is the weather today?
Agent:
1. Understand request
2. Decide weather information is needed
3. Call Weather API
4. Generate response
5. Return answer
AI Agent Frameworks for Kotlin
You can build agents manually, but frameworks can simplify development.
Popular options:
- LangChain integrations
- Spring AI
- Kotlin + OpenAI SDK
- Ktor-based AI services
Real-World Kotlin AI Agent Ideas
You can build:
- AI customer support assistant
- Android voice assistant
- AI coding assistant
- Document analysis agent
- Smart email assistant
- Personal productivity agent
Conclusion
AI agents combine:
- LLMs for intelligence
- Memory for context
- Tools for actions
- Kotlin for reliable application development
With Kotlin, developers can build powerful AI-powered Android applications and backend services by connecting language models with real-world data and APIs.
#kotlin
#android
#ai
#machinelearning
#programming
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