Observability for AI Systems

AI Observability Needs Application Context

Traditional logs are not enough for AI applications. Engineers need visibility into prompts, retrieved context, model responses, tool calls, validation failures, latency, cost, and user outcomes.

What to Track

• Prompt version, model version, parameters, and system instructions.
• Retrieved documents, metadata, rank, filters, and citation usage.
• Tool calls requested, rejected, executed, and failed.
• Structured output validation failures and retry behavior.
• Latency, token usage, cost, cache hits, and rate limits.
• User feedback, escalation events, manual overrides, and downstream impact.

Use Traces to Debug Behavior

When an AI system fails, the answer alone is not enough. You need the full trace from input to retrieval to model call to tool execution to output validation.