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Interview Q&A · 23 questions · 5 answered publicly

Agents Interview Questions

Ordered by how often they're asked. Sign in free to see every answer, rate your confidence and run timed mock interviews.

  1. Most asked Agents leaf: What is the ReAct pattern?

    Summary

    Interleave reasoning traces with tool actions in a loop until a final answer.

    Longer Answer

    Interleave reasoning traces with tool actions in a loop until a final answer. Good for short-horizon exploratory tasks.

    Key Points

    • Interleave reasoning traces with tool actions in a loop until a final answer
    • Good for short-horizon exploratory tasks
    • Core agent pattern
    • Use the answer to connect the concept back to a realistic production or interview scenario for 'Agents leaf: What is the ReAct pattern?'

    Comparison / Tradeoffs

    Aspect Notes
    Core idea Interleave reasoning traces with tool actions in a loop until a final answer.
    Practical tradeoff Core agent pattern.

    Permalink

  2. Most asked Agents leaf: When should you avoid agents?

    Summary

    When the workflow is known, latency/cost budgets are tight, or side effects need strict control-use single-shot LLM or deterministic code.

    Longer Answer

    When the workflow is known, latency/cost budgets are tight, or side effects need strict control-use single-shot LLM or deterministic code. Architecture judgment.

    Key Points

    • When the workflow is known, latency/cost budgets are tight, or side effects need strict control-use single-shot LLM or deterministic code
    • Architecture judgment
    • Relevant areas: Ai, Agents, Architecture
    • Use the answer to connect the concept back to a realistic production or interview scenario for 'Agents leaf: When should you avoid agents?'

    Comparison / Tradeoffs

    Aspect Notes
    Core idea When the workflow is known, latency/cost budgets are tight, or side effects need strict control-use single-shot LLM or deterministic code.
    Practical tradeoff Architecture judgment.

    Permalink

  3. Most asked Agents leaf: How do you prevent infinite tool loops?

    Summary

    Max steps, token budgets, repeat-detection, timeouts, and forced finalization.

    Longer Answer

    Max steps, token budgets, repeat-detection, timeouts, and forced finalization. Log every iteration for debugging.

    Key Points

    • Max steps, token budgets, repeat-detection, timeouts, and forced finalization
    • Log every iteration for debugging
    • Control loop hygiene
    • Use the answer to connect the concept back to a realistic production or interview scenario for 'Agents leaf: How do you prevent infinite tool loops?'

    Comparison / Tradeoffs

    Aspect Notes
    Core idea Max steps, token budgets, repeat-detection, timeouts, and forced finalization.
    Practical tradeoff Control loop hygiene.

    Permalink

  4. Most asked Agents leaf: What is structured function calling?

    Summary

    Model emits name+args matching a schema; runtime validates and executes.

    Longer Answer

    Model emits name+args matching a schema; runtime validates and executes. Prefer JSON schema enforcement over free-form text.

    Key Points

    • Model emits name+args matching a schema; runtime validates and executes
    • Prefer JSON schema enforcement over free-form text
    • Tool calling basics
    • Use the answer to connect the concept back to a realistic production or interview scenario for 'Agents leaf: What is structured function calling?'

    Comparison / Tradeoffs

    Aspect Notes
    Core idea Model emits name+args matching a schema; runtime validates and executes.
    Practical tradeoff Tool calling basics.

    Permalink

  5. Most asked Agents leaf: What is tool hallucination?

    Summary

    Model invents tool names/args.

    Longer Answer

    Model invents tool names/args. Mitigate with strict schemas, constrained decoding, and runtime rejection of unknown tools.

    Key Points

    • Model invents tool names/args
    • Mitigate with strict schemas, constrained decoding, and runtime rejection of unknown tools
    • Common failure mode
    • Use the answer to connect the concept back to a realistic production or interview scenario for 'Agents leaf: What is tool hallucination?'

    Comparison / Tradeoffs

    Aspect Notes
    Core idea Model invents tool names/args.
    Practical tradeoff Common failure mode.

    Permalink

  6. Agents leaf: How should tool schemas be designed?
  7. Agents leaf: How do you evaluate agent task success?
  8. Agents leaf: How do you sandbox tool execution?
  9. Agents leaf: Compare planner-executor vs ReAct.
  10. Agents leaf: What is human-in-the-loop for agents?
  11. Agents leaf: How do multi-agent systems fail?
  12. Agents leaf: Design short-term vs long-term agent memory.
  13. Agents leaf: Deterministic code vs LLM for a step?
  14. Agents leaf: How do you recover from a failed tool call?
  15. Agents leaf: What telemetry is required for agents in production?
  16. Agents leaf: How do you handle long-running workflows?
  17. Agents leaf: How do you cost-control agentic features?
  18. Agents leaf: Explain observation formatting best practices.
  19. Agents leaf: How do swarm/crew patterns differ from a single agent?
  20. Agents leaf: Compare assistants API threads vs custom orchestration.
  21. Compare ReAct-style agents, plan-and-execute agents, and multi-agent systems. When would you use each?
  22. How does tool/function calling work with LLMs, and what design mistakes cause unreliable agents?
  23. How would you design memory for a long-running AI agent?