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Agentic AI Interview Questions (Agent Architectures & Frameworks)

1. Fundamentals of Agentic AI

  1. What is Agentic AI, and how is it different from a traditional LLM-based system?

  2. What are the core components of an AI agent?

  3. Explain the concept of autonomy in agentic systems.

  4. How do goals, plans, actions, and feedback loops interact in an AI agent?

  5. What is the difference between reactive agents and deliberative agents?

  6. What is multi-step reasoning, and why is it critical for agents?

  7. Explain sense–think–act architecture in Agentic AI.

  8. How do agents maintain state across interactions?

  9. What is the role of memory in agent-based systems?

  10. Why are LLMs alone insufficient for building fully agentic systems?

2. Agent Architectures & Frameworks

  1. Explain the ReAct (Reason + Act) pattern.

  2. What is the difference between AutoGPT and BabyAGI?

  3. How does LangGraph differ from LangChain?

  4. What is CrewAI, and when would you prefer it?

  5. Explain single-agent vs multi-agent architectures.

  6. What are planner–executor–critic architectures?

  7. How do agents coordinate in multi-agent collaboration?

  8. What is a tool-augmented agent?

  9. How do event-driven agents work?

  10. Explain hierarchical agents with an example.

3. Planning, Reasoning & Decision-Making

  1. What is task decomposition, and how is it implemented?

  2. Compare symbolic planning vs LLM-based planning.

  3. What is chain-of-thought, and how does it differ from agent planning?

  4. How do agents decide when to stop?

  5. Explain reflection and self-critique in agents.

  6. What is meta-reasoning in agentic systems?

  7. How do agents handle uncertainty and incomplete information?

  8. What is the role of reward signals in agent decision-making?

  9. How do agents avoid hallucinated actions?

  10. Explain goal re-prioritization in long-running agents.

4. Tools, Memory & Environment Interaction

  1. What types of memory exist in agentic AI?

  2. How does vector memory differ from symbolic memory?

  3. How do agents select the right tool for a task?

  4. What is tool grounding, and why is it important?

  5. How do agents interact with external APIs and systems?

  6. Explain environment simulation for agent testing.

  7. What is episodic memory vs semantic memory?

  8. How do agents recover from tool failures?

  9. What is context compression, and why is it needed?

  10. How do agents persist long-term memory efficiently?

5. Multi-Agent Systems

  1. What problems are best suited for multi-agent systems?

  2. How do agents communicate with each other?

  3. What is role-based agent design?

  4. How do you prevent agent conflict or deadlocks?

  5. Explain centralized vs decentralized control.

  6. What is emergent behavior in multi-agent systems?

  7. How do agents negotiate or collaborate on tasks?

  8. What is agent voting or consensus?

  9. How do you scale multi-agent systems?

  10. What are failure modes unique to multi-agent AI?

6. Evaluation, Reliability & Safety

  1. How do you evaluate agent performance?

  2. What metrics are used for agentic systems?

  3. How do you test agents in non-deterministic environments?

  4. What is agent alignment, and why is it hard?

  5. How do you implement guardrails for agents?

  6. Explain sandboxing in agent execution.

  7. How do you detect and prevent runaway agents?

  8. What is human-in-the-loop in agentic AI?

  9. How do you ensure reproducibility?

  10. What is over-automation risk?

7. Real-World System Design Questions

  1. Design an AI research agent that searches, summarizes, and cites papers.

  2. Design an autonomous customer support agent with escalation.

  3. Design a code-writing agent with self-review and testing.

  4. How would you build an AI project manager agent?

  5. Design a web-browsing autonomous agent safely.

  6. How would you architect a multi-agent DevOps system?

  7. Design an agent that learns from user feedback over time.

  8. How do you limit cost and latency in agent workflows?

  9. How do you secure agents with access to credentials?

  10. How would you deploy agents in production?

8. Ethics, Governance & Future Trends

  1. What are the ethical risks of autonomous agents?

  2. How do you prevent goal misalignment?

  3. What is AI agency vs responsibility?

  4. How do regulations impact Agentic AI?

  5. What is explainability in agent decisions?

  6. Can agentic systems be legally accountable?

  7. What are the risks of self-improving agents?

  8. How does Agentic AI relate to AGI?

  9. What safeguards are required for enterprise agents?

  10. What is the future of Agentic AI in 5 years?

9. Coding & Practical Questions (Common in Interviews)

  1. Implement a ReAct-style agent (high-level design).

  2. How would you store and retrieve agent memory efficiently?

  3. Write pseudo-code for a planner–executor loop.

  4. How do you debug an agent that makes wrong decisions?

  5. How do you handle LLM failures inside agents?

  6. How do you control token explosion?

  7. How would you mock tools during testing?

  8. How do agents handle concurrent tasks?

  9. Explain idempotency in agent actions.

  10. How do you log agent reasoning safely?

10. Advanced / Research-Level Questions

  1. What is emergent agency?

  2. How does Agentic AI differ from RL agents?

  3. What is world modeling in agents?

  4. How do agents learn from experience without retraining?

  5. What are constitutional agents?

  6. Explain self-refining agents.

  7. What are limitations of current agent frameworks?

  8. How do agents reason across long horizons?

  9. What is toolformer-style training?

  10. What open research problems exist in Agentic AI?

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