Debugging a silently failing AI workflow with Agent Core full-link observability
The most difficult AI workflows to debug are the ones that don't crash. A multi-step agent pipeline can quietly return a plausible-looking answer that's actually wrong, without ever throwing an error. One broken step early in the chain compounds into a wrong final output, and standard monitoring rarely catches it: the run finishes, the logs show green, and the problem only surfaces hours later…
Debugging AI workflows can be challenging when they don't crash. A multi-step agent pipeline can quietly produce a plausible-looking answer that is actually incorrect, without raising any errors. Many monitoring systems fail to detect this issue as the run finishes without any warnings. This tutorial demonstrates debugging a specific case using openJiuwen Agent Core v0.1.18.
The process involves instrumenting a three-node pipeline (fetch → analyze → summarize) with OTEL-compatible spans, which are structured traces that record what each step does. The output from each step (latency, token counts, and a per-step failure flag) is then read back to pinpoint the exact node where the chain went wrong.
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