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Agent reliability: evals, failure modes and observability

  • Agents
  • Asked in most interview loops
  • About 1 h 35 min to learn
  • 4 vetted resources
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What this topic covers

Once an agent loop works in a demo, the real job is making it trustworthy: deciding what “success” means for a multi-step task, grading outcomes rather than exact paths, tracing every tool call so failures can be diagnosed, and knowing when a human must confirm an action. Interviewers probe this with questions like “how would you know your agent got worse after a prompt change?”, “what does your eval set look like and where did the tasks come from?”, “how do you handle non-determinism?” and “which actions need a human in the loop?”. Strong answers separate capability evals from regression evals, name concrete graders (code checks, model judges, humans), describe traces and spans, and show a loop where production failures become new test cases.

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You’re ready when you can

What interviewers check, from the resources below.

  • Define task, trial, grader, transcript and harness, and say what an agent eval adds over a single-turn eval
  • Compare code-based, model-based and human graders and give one case where each is the right choice
  • Explain the difference between a capability eval and a regression eval and why both are needed
  • Explain pass@k vs pass^k and which one a reliability-critical product cares about
  • Describe how to bootstrap a first eval set (how many tasks, where they come from, how to keep it from saturating)
  • Explain what a trace and a span are for an agent run and what you'd put in each
  • List the metrics you'd put on an agent dashboard (latency, cost, error rate, feedback, accuracy) and why each matters
  • Contrast offline and online evaluation, including what each can and cannot catch
  • Describe the loop that turns a production failure into a new offline test case
  • Explain why agents should be graded on end state or outcome rather than on the exact sequence of steps

What to study, in order

Each link opened and checked by hand; the date says when.

  1. Start here

    Demystifying evals for AI agents (opens in a new tab)

    Anthropic Engineering · Mikaela Grace, Jeremy Hadfield, Rodrigo Olivares, Jiri De Jonghe · 45 min · Free · checked 21 Oct 2026

    Why
    This is the vocabulary (task, trial, grader, transcript, harness, pass@k vs pass^k) interviewers expect when they ask how you would evaluate an agent.
    What to read
    Read “The structure of an evaluation” and “Why build evaluations?”, then “How to evaluate AI agents” down through “How to think about non-determinism in evaluations for agents”, then “Going from zero to one: a roadmap to great evals for agents”.
    What to skip
    “Appendix: Eval frameworks” and the agent-type subsections that don't match your target role (e.g. “Computer use agents”); skim “How evals fit with other methods for a holistic understanding of agents” only for the comparison of evals with monitoring and A/B tests.

  2. Free alternative

    AI Agent Observability and Evaluation (opens in a new tab)

    Hugging Face Agents Course · Hugging Face (Ben Burtenshaw, Joffrey Thomas, Thomas Simonini, Sergio Paniego) · 20 min · Free · checked 21 Oct 2026

    Why
    Gives you the traces-and-spans mental model and the offline-vs-online evaluation split in twenty minutes, which is enough to answer “how would you monitor this agent in production?”.
    What to read
    Read “What is Observability?” through “Combining the two”, paying attention to “Traces and Spans”, “Key Metrics to Monitor”, “Offline Evaluation” and “Online Evaluation”.
    What to skip
    The tool shopping list under “Observability Tools” and the Colab notebook linked from “Lets see how this works in practice”, unless you want hands-on Langfuse practice.

  3. Go deeper

    How we built our multi-agent research system (opens in a new tab)

    Anthropic Engineering · Jeremy Hadfield, Barry Zhang, Kenneth Lien, Florian Scholz, Jeremy Fox, Daniel Ford · 30 min · Free · checked 21 Oct 2026

    Why
    A real production case study of agent failure modes (compounding errors, long-running state, deploys mid-run) is the best source of war stories for “tell me about a time an agent broke” and “how would you make it reliable?”.
    What to read
    Read “Effective evaluation of agents”, “Production reliability and engineering challenges” and the “Appendix” (end-state evaluation, long-context management, writing subagent output to files).
    What to skip
    “Benefits of a multi-agent system” and “Architecture overview for Research” if you've already done the agent-loops topic; skim “Prompt engineering and evaluations for research agents” for the tool-design tips only.

  4. Go deeper

    Inspect (opens in a new tab)

    GitHub · UK AI Security Institute · 45 min · Free · checked 22 Oct 2026

    Why
    Inspect, from the UK AI Security Institute, is widely used for agent and safety evals. Its dataset, solver and scorer split is a clean vocabulary for describing any agent eval in an interview.
    What to read
    Read examples/hello_world.py and examples/theory_of_mind.py: a Task built from a dataset, a solver chain (chain_of_thought, generate, self_critique) and a scorer (exact, model_graded_fact). Run one with inspect eval and open the log in inspect view. Then read src/inspect_ai/agent/_react.py to see how an agent solver runs tools in a loop.
    What to skip
    Skip the development setup, the TypeScript frontend submodule and the docs-building sections of the README; browse the 200+ prebuilt evals only for ideas.

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