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Advanced
Agent Reliability and Long-Horizon Robustness
Long-horizon agents fail because per-step success compounds: a 95 percent reliable step is only about 60 percent reliable over ten steps. Reliability engineering covers consistent completion (not just pass@k), error recovery, step and token budgets, human-in-the-loop checkpoints, and containing cascading failure in multi-agent systems. Applied AI interviews probe this to separate people who built a demo from people who shipped an agent that holds up over thousands of runs.
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PRACTICE THIS IN REAL QUESTIONS
RAG & Agent System DesignWhy do agents fail on long-horizon tasks, and how do you keep reliability up over many steps?→RAG & Agent System DesignHow do you design human-in-the-loop checkpoints so an agent can pause, ask, and resume?→RAG & Agent System DesignWhen do you build an agent instead of a single LLM call, and how do you keep a multi-step agent reliable?→AI Security, Privacy & GovernanceWhat are adversarial examples, why are they a security concern, and how do you defend against them?→RAG & Agent System DesignWhen do you use a multi-agent system, and what orchestration patterns and pitfalls matter?→System Design for AI in ProductionDesign a deep research agent that answers complex questions by searching and synthesizing many sources.→
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