How a Single AI Agent Error Can Cascade Into a Fully Failed Run
When building LLM-based agents, a common assumption is that errors at each step are independent — but in practice, one mistake can corrupt every step that follows. Because agents continuously feed their own past outputs back as future inputs, a wrong fact or bad action entered into the transcript becomes a flawed foundation for all subsequent reasoning. This cascading failure mode differs from distributed system failures, which spread horizontally across many workers; agent cascades spread vertically through time within a single run. Three key propagation mechanisms include poisoned premises (wrong facts treated as established), error residue (failed attempts anchoring future reasoning), and committed actions (real-world changes that cannot be undone). The structural fix lies in identifying where in a trajectory context contamination can be intercepted or isolated before it compounds further.
