AI Operations and Observability

Overview

Operational excellence for AI systems requires visibility into quality, latency, token usage, and cost. Standard API monitoring is not enough.

Observability Model

flowchart TD
    A[Request Telemetry] --> B[Prompt and Context Metadata]
    B --> C[Model Metrics]
    C --> D[Quality Signals]
    D --> E[Dashboards and Alerts]

Practical Example - Structured Telemetry in C#

public sealed class AiTelemetry
{
    private readonly ILogger<AiTelemetry> _logger;

    public AiTelemetry(ILogger<AiTelemetry> logger)
    {
        _logger = logger;
    }

    public void LogInference(string model, int promptTokens, int completionTokens, long latencyMs, bool success)
    {
        _logger.LogInformation(
            "ai_inference model={Model} prompt_tokens={PromptTokens} completion_tokens={CompletionTokens} latency_ms={LatencyMs} success={Success}",
            model,
            promptTokens,
            completionTokens,
            latencyMs,
            success);
    }
}

Production Example - Alerting

Useful alerts include:

• Sudden increase in hallucination reports
• Latency spikes for a specific model deployment
• Unexpected token consumption by tenant
• Rapid growth in failed safety checks

Common Pitfalls

• Tracking only latency and ignoring quality drift
• No correlation ID connecting retrieval and generation steps
• No cost observability by feature or tenant

Key Takeaways

• AI observability must include quality and cost signals.
• Structured telemetry enables troubleshooting and governance.
• Alerting should cover reliability, safety, and business impact.