LLM traces
Observe the behaviour that matters and turn meaningful failures into engineering feedback.
PHOENIX · ARIZE · AI OBSERVABILITY
Phoenix Arize for AI quality and evaluation workflows
AI observability can help teams understand traces, retrieval, model behaviour and production failures. PARIMI connects observability signals back to testing and evaluation.
RAG retrieval behaviour
Observe the behaviour that matters and turn meaningful failures into engineering feedback.
Agent workflows
Observe the behaviour that matters and turn meaningful failures into engineering feedback.
Evaluation signals
Observe the behaviour that matters and turn meaningful failures into engineering feedback.
Production failure discovery
Observe the behaviour that matters and turn meaningful failures into engineering feedback.
Regression feedback loops
Observe the behaviour that matters and turn meaningful failures into engineering feedback.
Observability becomes useful when it improves quality.
Use traces and evaluation signals to identify failure modes, strengthen regression suites and improve release confidence.