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Why Agent Systems Fail, and How a Lab Would Find OutWhy Agent Systems Fail, and How a Lab Would Find Out

An agent that passes its demo can still fail most real tasks, usually for arithmetic reasons: reliability compounds as p to the n, pass@k and pass^k answer different questions, and a one-run benchmark gap can sit inside its own noise. You read and write postmortems, sort failures by harness layer, and build the control four agent shapes share: an evidence gate, deterministic code that refuses a claim without its evidence. The capstone is an interview work sample: a manifest, a verifier, a human gate, one defended failure mode and an evaluation with error bars. The companion lab in the public profrodai-resources repo runs offline on Python's standard library.

MethodThis course teaches a discipline. The examples use today's tools; the method is meant to outlast them.

88 min read
10 lessons
Rod Rivera

Rod Rivera

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AI agentsagent evaluationharness engineeringfailure analysisinterview preparation

What You'll Learn

  • Derive why per-step reliability compounds (p to the n) and what a retry with a verifier changes
  • Tell pass@k from pass^k, and explain why a measured pass^k sits above the independence prediction
  • Put a binomial error bar on an agent success rate and judge whether a benchmark gap is signal
  • Read a public postmortem for symptom, impact, root cause, the eval that missed it, and the fix
  • Write a failure analysis in the shape an interview loop or a take-home work sample can probe
  • Sort agent failures by harness layer (tools, state, retries, context, evaluation, oversight)
  • Build an evidence gate, a deterministic verifier that refuses a claim without its source
  • Check a model grader's calibration bucket by bucket, with an interval on every bucket
  • Explain why a policy-enforced conversational agent keeps decisions out of the language model

Prerequisites

  • Comfort reading Python and YAML
  • Basic probability (independent events, a binomial proportion); the course derives the rest
  • No prior agent-building experience required; the book's agent loop chapter is the companion

Syllabus

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