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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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TAUGHT IN THESE COURSES
This concept is a reference. These lessons put it in sequence, with what comes before and after it.
Agent Engineering·The single-agent loopFree14mStopping is a design decision, not a budgetBudgets stop a runaway agent. They do not tell it when it is finished, and the difference between those two things is where most agent quality is lost. This lesson covers the four ways to define done and what to return when you never got there.Agent Engineering·Tools as an interfaceFree13mWriting errors an agent can act onAn error message is a prompt. It is the only instruction you get to inject at the exact moment the agent is deciding what to do next, and most systems waste it on a stack trace.Applied AI Engineering·Tools and your first agentSign in14mThe loop, and how to stop itPutting a tool call in a loop is a four-line change that turns a predictable component into an open-ended process. This lesson covers what the loop buys, the three ways it fails to terminate, and the budgets that keep it survivable.
RELATED CONCEPTS
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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