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Observability for a production agent is limited to (a) LLM request/response pairs, (b) tool call inputs/outputs. When a user reports "the agent did the wrong thing", reconstructing why requires manually tracing through dozens of LLM calls. Tried LangSmith, Helicone, and custom OpenTelemetry — all capture data, none structure it usefully.
Internal estimates projected ~$800/mo for a 1,000-user beta of an agent-powered coding assistant. Actual month 1 was $8,900. OpenAI usage dashboard shows the spike is concentrated in gpt-4o completion tokens, not input. Mean conversation length is 12 turns.