Create an observability platform for multi-step AI workflows that reveals where tasks fail, consume unnecessary resources, lose context, or produce unreliable results.
Allow developers to define workflows containing model calls, retrieval steps, tools, branching conditions, human approvals, and output validation. Record each step's input references, output metadata, latency, estimated cost, retry count, and validation status.
Build a trace viewer that shows the complete execution path, including failed branches, repeated calls, missing data, and unexpected tool responses. Identify steps that contribute disproportionately to cost or delay.
Let users replay workflows with fixed mock responses, compare different prompt versions, and test changes against a set of representative cases. Include configurable budgets, stop conditions, and human approval gates.
Redact sensitive values from logs, keep secrets out of traces, and distinguish measured costs from estimates. Never claim that an AI output is correct merely because the workflow completed successfully.
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