Observability
Our infrastructure layer records enough runtime information to understand how production workloads behave across models, tools, services, and external dependencies.
What is recorded
Observability can include logs, metrics, traces, latency, failures, model usage, and tool activity.
Without this layer, an AI workflow can fail in ways that are visible only as a wrong answer or a missing action.
End-to-end trace
A single user or automated task may cross multiple components:
- Request
- Retrieval
- Model
- Tool
- External API
- Validation
- Workflow state
Tracing connects those stages so engineers can determine where time was spent, which component failed, and which versions participated in the execution.
Logs provide event detail. Metrics show aggregate behavior. Traces connect one execution across components. They answer different questions.
AI-specific signals
Model-driven systems introduce useful telemetry beyond ordinary service uptime. We may need to observe model endpoint selection, token or inference usage, validation failures, fallback frequency, tool calls, retry rates, and escalation volume.
A rising fallback rate can indicate a provider problem or model regression even when the top-level service still returns responses.
Operational diagnosis
Observability should make failures localizable. If latency increases, we want to know whether the delay comes from retrieval, queueing, model inference, an external API, or repeated validation and retry.
If an automated action fails, we want the execution path rather than only the final error message.
Tradeoff
Telemetry has cost. Excessive logs consume storage, can duplicate sensitive data, and can create more noise than signal. We therefore design observability around useful operational questions rather than recording every possible payload.
The objective is enough visibility to detect, diagnose, and measure production behavior without turning the telemetry system into another uncontrolled data lake.
Service-level behavior
Observability becomes most useful when telemetry is connected to an expected service level. For one workflow, the important signal may be p95 latency. For another, it may be batch completion time, failed tool calls, queue age, fallback frequency, or the percentage of cases escalated to humans. The system can then alert on degradation in the behavior users actually depend on rather than on raw infrastructure utilization alone.
AI components also need version dimensions in telemetry. If failure rate increases after a model, prompt, retrieval index, or tool revision, traces and metrics should make that change visible.
This turns observability into more than incident response. It becomes part of evaluation in production: a way to detect when the real workload distribution begins behaving differently from the conditions measured before deployment.