Capture the execution evidence behind every outcome.
Inspect prompts, retrieval, tool calls, model responses, evaluator signals, latency, and token use in one trace model built for multi-step AI systems.
Apply the current billing policy. Use tools only when the customer record is required.
Leave this stage with evidence the next one can use.
Each stage is configured around the customer's application, risk, quality standard, operational ownership, and release policy.
Nested execution
Move from session to trace to span without losing prompt, model, or tool context.
Version-aware evidence
Link every span to the prompt, model, configuration, and deployment that produced it.
Operational search
Filter by environment, release, feature, customer segment, failure mode, latency, score, or cost.
Failure reconstruction
Preserve errors, retries, tool results, retrieval evidence, and evaluator explanations for review.
No stage operates in isolation.
The evidence links traces, versions, evaluation runs, release gates, production monitors, and cost attribution.
Practical thinking for production AI quality.
Designing a trace schema for multi-step agents
A practical trace model for prompts, retrieval, tools, model calls, retries, evaluator signals, and user outcomes.
Using OpenTelemetry for generative AI observability
Adopt open trace context and GenAI semantic conventions while preserving product-specific evaluation and cost fields.
Evaluate retrieval coverage inside the trace
Inspect query construction, candidate sources, ranking, access filters, context selection, and answer grounding as one path.
Start with the traces behind a production outcome.
Map the execution path, identify missing context, and agree the trace evidence required before evaluation begins.