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Advanced
Agent Reliability and Long-Horizon Robustness
Agents over long horizons break down because per-step reliability multiplies: a step that works 95 percent of the time drops to roughly 60 percent across ten steps. The discipline spans consistent completion (not pass@k), recovering from errors, step and token budgets, human-in-the-loop checkpoints, and stopping cascading failure inside multi-agent systems. AI, ML, and GenAI engineer interviews test this to tell apart people who built a demo from people who shipped an agent that survives thousands of runs.
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