Building with Kotlin Coroutines: Why We Chose Kotlin for the AMX Engine

When we started building the AMX Engine, we had a choice: stick with Java (which our team knew well) or jump to Kotlin. Six months later, I can’t imagine building our agent orchestration system any other way.

The decision wasn’t obvious at first. We had a Java team, Java infrastructure, and plenty of Java libraries. But managing thousands of AI agents simultaneously? That’s where Kotlin coroutines changed everything.

Why Not Just Java?

We tried Java first. Our initial prototype used CompletableFutures and thread pools to handle agent communication. Here’s what that looked like:

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// Java approach - gets messy fast
CompletableFuture<AgentResponse> future1 = agent1.processMessage(request)
    .thenCompose(response1 -> agent2.processMessage(response1))
    .thenCompose(response2 -> agent3.processMessage(response2));

// Error handling becomes a nightmare
future1.exceptionally(throwable -> {
    // What do we do here? Which agent failed?
    return fallbackResponse;
});

With hundreds of agents talking to each other, the callback hell was real. Debugging async flows was like playing detective in a house of mirrors. Thread management became a constant headache, especially when agents had different processing patterns.

Some agents needed quick responses, others did heavy computation. Traditional thread pools couldn’t adapt to these varying workloads efficiently.

Enter Kotlin Coroutines

Coroutines gave us something we desperately needed: async code that reads like sync code. Here’s the same agent orchestration in Kotlin:

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// Kotlin coroutines - much cleaner
suspend fun processAgentWorkflow(request: AgentRequest): AgentResponse {
    try {
        val response1 = agent1.processMessage(request)
        val response2 = agent2.processMessage(response1)
        val response3 = agent3.processMessage(response2)
        return response3
    } catch (e: AgentException) {
        // Clear error handling with proper context
        handleAgentError(e, request)
        return fallbackResponse
    }
}

The difference was night and day. Our code became readable, debuggable, and maintainable. But the real power showed when we needed concurrent agent communication:

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// Parallel agent processing
suspend fun orchestrateAgents(request: MultiAgentRequest): CombinedResponse {
    val responses = withContext(Dispatchers.IO) {
        listOf(
            async { nlpAgent.analyze(request.text) },
            async { visionAgent.process(request.image) },
            async { reasoningAgent.infer(request.context) }
        ).awaitAll()
    }
    
    return combineResponses(responses)
}

The Learning Curve

Adopting Kotlin wasn’t painless. Our team had to learn new patterns and unlearn some Java habits.

Structured Concurrency was the biggest mental shift. In Java, we were used to fire-and-forget thread spawning. Kotlin coroutines encourage a more disciplined approach:

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// Bad pattern from our Java days
fun badAgentLaunching() {
    GlobalScope.launch { // Don't do this
        agent.processForever()
    }
}

// Better structured approach
class AgentManager(private val scope: CoroutineScope) {
    fun startAgent(agent: Agent) {
        scope.launch {
            try {
                agent.processMessages()
            } catch (e: CancellationException) {
                agent.cleanup()
                throw e
            }
        }
    }
}

Context Switching took time to understand. We learned the hard way that Dispatchers.Default isn’t always the right choice:

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class AgentOrchestrator {
    // CPU-intensive agent processing
    suspend fun processAgent() = withContext(Dispatchers.Default) {
        heavyComputationAgent.analyze(data)
    }
    
    // I/O operations to external services
    suspend fun callExternalAPI() = withContext(Dispatchers.IO) {
        externalService.query(params)
    }
    
    // Main thread for UI updates (if needed)
    suspend fun updateUI() = withContext(Dispatchers.Main) {
        ui.updateAgentStatus(status)
    }
}

Agent Orchestration Patterns

Coroutines enabled patterns that would have been painful in Java:

Fan-out/Fan-in for Agent Coordination:

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suspend fun coordinateAgentTeam(task: ComplexTask): TeamResult {
    val subtasks = task.split()
    
    // Fan out to multiple agents
    val results = subtasks.map { subtask ->
        async {
            findBestAgent(subtask.type).process(subtask)
        }
    }.awaitAll()
    
    // Fan in - combine results
    return TeamResult.combine(results)
}

Timeout Handling for Unreliable Agents:

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suspend fun processWithTimeout(agent: Agent, request: Request): Response {
    return withTimeoutOrNull(5000) {
        agent.process(request)
    } ?: throw AgentTimeoutException("Agent ${agent.id} timed out")
}

Circuit Breaking for Agent Health:

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class AgentCircuitBreaker(private val agent: Agent) {
    private var failureCount = 0
    private var lastFailure: Long = 0
    
    suspend fun safeProcess(request: Request): Response? {
        if (isCircuitOpen()) {
            return null
        }
        
        return try {
            val response = agent.process(request)
            onSuccess()
            response
        } catch (e: Exception) {
            onFailure()
            null
        }
    }
}

Performance Surprises

Coroutines weren’t just about code clarity - they improved performance in ways we didn’t expect.

Memory Usage: Thread pools for thousands of agents consumed significant memory. Coroutines are lightweight - we can run 10,000+ concurrent agents on modest hardware.

Resource Utilization: The adaptive scheduling in coroutines meant our CPU cores stayed busier. Agents that were waiting for I/O didn’t hold threads hostage.

Startup Time: Cold starts for new agents became much faster. No thread pool warm-up, no complex initialization - just suspend functions ready to run.

What We’re Still Figuring Out

Debugging Async Flows: While better than Java, debugging coroutine stacks still requires specialized knowledge. Stack traces can be confusing, especially for junior developers.

Error Propagation: Getting error handling right across multiple agent layers remains tricky. CancellationException behavior still catches us off guard sometimes.

Testing Patterns: Unit testing suspending functions required learning new patterns. TestScope and runTest helped, but it was a learning curve.

Memory Leaks: It’s possible to create memory leaks with coroutines if you’re not careful about scope management. We’ve had a few production issues here.

Would We Choose Kotlin Again?

Absolutely. The productivity gains from readable async code, combined with the performance benefits, made Kotlin coroutines essential for our agent orchestration.

That said, it’s not magic. You still need to understand concurrency, still need to design systems carefully, and still need to handle edge cases properly. Kotlin just makes it more manageable.

For teams building AI agent systems, I’d recommend seriously considering Kotlin. The learning investment pays off when you’re orchestrating complex, concurrent AI workflows.

The AMX Engine processes millions of agent interactions daily, and Kotlin coroutines are what make that scale possible without losing our sanity.