Agent 与自主系统

题集来源:AMIT SHEKHAR(amitshekhariitbhu)收录项目:ai-engineering-interview-questions查看原题集

题目目录

54 道原题
  1. What is an AI agent, and how does it differ from a simple LLM call?

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  2. AI Agent Memory

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  3. Harness Engineering in AI

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  4. What matters more for an agentic coding tool like Claude Code: the model or the harness?

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  5. Explain the ReAct (Reasoning + Acting) agent architecture.

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  6. What is the Plan-and-Execute agent pattern?

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  7. What is tool use (function calling) in LLMs, and how does it enable agents?

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  8. What is the difference between structured output and function calling?

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  9. How do you design and define tools for an AI agent?

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  10. How does an agent decide when to call a tool versus answering from its own knowledge?

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  11. What is the difference between single-agent and multi-agent systems?

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  12. When do multi-agent systems break down, and when is a single agent the better choice?

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  13. What is Model Context Protocol (MCP), and how does it standardize tool integration?

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  14. How does MCP differ from traditional function calling?

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  15. What are AI SubAgents?

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  16. What are the different types of agent memory (short-term, long-term, episodic)?

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  17. How do you handle agent failures and implement error recovery?

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  18. What is an agent loop, and how does it decide when to stop?

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  19. Context Engineering

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  20. How does context compaction work?

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  21. Loop Engineering

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  22. Graph Engineering

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  23. How AI Agents Communicate?

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  24. What are Agent Skills?

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  25. How do you evaluate and test AI agents?

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  26. What are the security risks of agentic systems, and how do you mitigate them?

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  27. Your agent reads untrusted content (emails, web pages, documents) and can call tools. How do you prevent indirect prompt injection and data exfiltration?

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  28. What is the difference between reactive and proactive agents?

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  29. How do you manage token consumption and cost in long-running agent workflows?

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  30. What is the human-in-the-loop pattern for agents, and when is it needed?

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  31. How do you implement guardrails for AI agents to prevent harmful actions?

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  32. What is agent reflection, and how does it improve agent performance?

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  33. What is the difference between code-generating agents and tool-calling agents?

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  34. How do you handle multi-modal inputs and outputs in agentic systems?

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  35. How do you implement state management in complex agent workflows?

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  36. How do you build a customer support agent with escalation logic?

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  37. What is agent orchestration, and how do you implement it?

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  38. What is Sakana Fugu, and how does it orchestrate a team of AI models?

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  39. How do you build a code execution agent safely using sandboxed environments?

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  40. Your AI agent is stuck in an infinite loop. How do you detect and break the cycle?

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  41. Your AI agent gets conflicting answers from different tools. How does it reconcile them?

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  42. Your AI agent burns too many tokens per task. How do you reduce token consumption?

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  43. Your AI agent keeps exceeding its budget per task. How do you enforce budget limits?

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  44. Your AI agent hallucinates tool capabilities and passes wrong inputs. How do you fix it?

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  45. Your AI agent deleted a production database. How do you prevent irreversible actions?

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  46. Your AI agent has many tools, but keeps picking the wrong one. How do you improve tool selection?

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  47. Your AI agent takes too long to complete a task. How do you speed it up?

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  48. Your long-running agent drifts after hours and confidently works on the wrong thing. How do you diagnose and fix it?

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  49. Your LLM selects the right tool but extracts the wrong parameters. How do you fix parameter extraction?

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  50. How do Computer-Use Agents work?

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  51. How do Browser Agents work?

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  52. How does LangChain work?

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  53. How does LangGraph work?

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  54. What is OKF (Open Knowledge Format)?

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