engineering · 1 min read
Workflow Observability: Tracing Every AI Step
AI workflows fail in surprising ways. Distributed tracing turns surprise into evidence. Here is how Bhogar AI traces every step end-to-end.
BABhogar AI TeamProduct & Engineering
Debugging an AI workflow without traces is debugging in the dark. Each LLM call, retrieval, tool invocation and decision should be a span in a single trace from trigger to result.
Why it matters
The problem is volume: trace data for AI workflows quickly outgrows naive backends. Production observability needs sampling, redaction and a UI that summarises rather than firehoses.
How Bhogar AI approaches it
Bhogar AI emits OpenTelemetry traces by default, with intelligent sampling (always-keep on errors and slow runs), in-flight PII redaction, and a built-in trace UI plus export to Datadog, Honeycomb and Grafana.
- OTel-native traces for every workflow run
- Always-keep sampling on errors and slow runs
- PII redaction at trace emit time
- Built-in trace UI with timeline and waterfall views
- Export to Datadog, Honeycomb, Grafana, New Relic
What you get
Teams with end-to-end traces resolve production incidents 3-5× faster than teams relying on logs alone.