Why Go for AI agents

When we started building agent infrastructure at Aktagon, we evaluated several languages. The decision came down to what matters most in production: reliability, observability, and deployment simplicity.

Concurrency without complexity

Go’s goroutines and channels map naturally to agent workflows. Each agent step runs as a goroutine. Communication between steps uses typed channels. No thread pools to tune, no async/await coloring, no callback hell.

func (a *Agent) Run(ctx context.Context, task Task) (Result, error) {
    results := make(chan StepResult, len(a.steps))
    for _, step := range a.steps {
        go step.Execute(ctx, task, results)
    }
    return a.collect(ctx, results)
}

Single binary deployment

go build produces a single static binary. No runtime, no dependencies, no container layers beyond scratch. This matters when deploying to edge locations or air-gapped healthcare environments.

Predictable performance

GC pauses in Go 1.22+ are sub-millisecond. Memory usage is predictable. CPU profiles are actionable. When an agent pipeline processes thousands of HL7 messages per second, you need to know where time goes.

The trade-off

Go lacks the rich ML ecosystem of Python. We don’t fight this — Python handles model inference, Go handles everything else: orchestration, routing, state management, API serving, and monitoring.