Multi-agent workflows fail by propagating small context errors, not weak models
Katsumonggo · reddit · 2026-09-24
A Reddit poster describes how multi-step AI workflows degrade even when each individual call is good: a later call misreads the previous output, and the next model builds on that mistake as if verified, producing polished results on bad context.
The fix is workflow design, not prompt tweaking:
- Keep tasks small and tightly scoped
- Pass only useful context forward
- Verify important outputs before the next model consumes them
- Retrieve missing information instead of letting the model guess
- Escalate to stronger models only when needed
- Retry only the failed step, not the whole pipeline
The author open-sourced the approach in the repo Jev-Orchestrated-Decision-Mesh (currently OpenAI-based, portable to Claude). His takeaway: multi-agent failures are usually about error propagation, not model intelligence.
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