Agentic AI Interview Questions – Part 2

Welcome to Part 2 of our Agentic AI Interview Questions Series. After covering the foundational principles of agentic AI in Part 1 like task decomposition, memory and ReAct prompting, we now dive into more advanced aspects of designing, evaluating and aligning autonomous agents.

If you’re targeting roles in cutting-edge AI research or product teams building autonomous workflows, these questions will solidify your readiness for deep technical interviews.

16. What are hierarchical agents and why are they useful?

Hierarchical agents break down complex goals into layers of sub-agents or tasks. This allows:

Modularity: Different agents specialize in subtasks (e.g., planning vs execution).
Scalability: Systems can handle more complex workflows by distributing logic.
Maintainability: Easier debugging and updates in isolated components.

Example:
A project management AI could have:

A planner agent to set milestones
A research agent to gather data
A reporting agent to compile updates

This structure mimics human delegation and leads to more robust, interpretable systems.

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

Reflection is a process where agents analyze their past behavior, draw insights, and adapt future strategies. It introduces a feedback loop that helps:

Catch and correct failures (e.g., invalid tool responses)
Improve future planning (learning from context)
Explain and justify actions (better interpretability)

Reflection can be:

Explicit: Prompting the LLM to reason about what went wrong
Automated: Updating memory with key takeaways post-task

This mechanism enables self-improving agents, critical for long-running tasks or dynamic environments.

18. What is a scratchpad in agentic reasoning?

A scratchpad is an internal space for storing intermediate reasoning steps. It’s commonly used in chain-of-thought and tool-use agents.

Why it’s important:

Improves task decomposition
Tracks dependencies between steps
Enhances interpretability and debugging

Agents use the scratchpad to “think aloud,” often structured like:

Thought: I need to fetch data before I can analyze it.
Action: Query[Company Revenue]
Observation: $500M
Next Step: ...

Scratchpads help agents avoid redundant calls, remember what they’ve done, and proceed systematically.

19. How is agent safety ensured in autonomous systems?

Ensuring agentic safety involves preventing:

Undesired behavior (e.g., infinite loops, harmful outputs)
Hallucinated tool calls
Overuse of resources

Techniques include:

Constraints and guards: Limit the number or type of actions per step
Rate limiting: Avoid API spamming
Human-in-the-loop: Review critical decisions
Safe fallback strategies: Use defaults or bailouts on failures
Intent verification: Ensure agent output aligns with user goals

Safety is a top priority, especially in enterprise, healthcare, or legal use cases.

20. What are planning strategies in agentic AI?

Agents use various planning methods:

Zero-shot planning: LLM infers the entire task plan in one go.
Iterative planning: Agent updates plan as new information becomes available.
Tree-based planning: Agent explores multiple branches and backtracks as needed (e.g., Tree of Thought).
Reinforcement planning: Agent learns optimal sequences based on reward feedback.

Each strategy balances trade-offs between efficiency, robustness, and exploration. For example, tree search is more thorough but computationally expensive.

21. What are evaluation metrics for agent performance?

Unlike standard NLP models, agent evaluation must consider:

Task success rate (Was the goal achieved?)
Step efficiency (How many actions did it take?)
Correctness of tool use or reasoning
Adaptability (Did the agent handle unexpected inputs?)
User satisfaction (In interactive systems)

Methods:

Simulated benchmarks (e.g., WebArena)
Real-world test suites with gold standard outputs
Human raters for qualitative judgment

Evaluation is a major research challenge due to the open-endedness of agentic tasks.

22. What is Tree of Thought (ToT) prompting?

Tree of Thought is a planning framework that enables agents to:

Branch into multiple reasoning paths
Evaluate alternatives
Choose the most promising outcome

It’s particularly useful in:

Puzzle solving
Game playing
Tool chains with multiple options

Example:

Goal: Find the fastest travel option
Thought Path A: Consider flights
Thought Path B: Consider trains
Score each → pick best

ToT helps build deliberative, planning-capable agents rather than reactive responders.

23. How do retrieval-augmented agents work?

Retrieval-augmented agents combine:

LLMs for reasoning and generation
Vector databases / search APIs to fetch relevant documents or code

Workflow:

Identify knowledge gap
Query retrieval system (e.g., FAISS, Elastic)
Inject result into scratchpad/context
Reason based on updated information

This method creates agents with dynamic, grounded knowledge, crucial in fast-changing domains (e.g., finance, law, medicine).

24. What are autonomous loops in long-running agents?

Autonomous loops are designed for:

Running tasks over long durations (e.g., daily updates)
Re-checking status (e.g., retry on tool failure)
Self-scheduling next steps (e.g., via cron logic or task queue)

Loops need to be:

Interruptible (manual override)
Memory-integrated (persist state)
Safe (prevent runaway behavior)

This is foundational in agentic workflows like RAG pipelines, personal assistant agents, and smart schedulers.

25. What is the future of agentic AI?

Agentic AI is evolving toward:

Multi-modal agents (text, voice, image inputs)
Collaborative teams of agents (agent-to-agent interaction)
Embodied agents (robots, AR/VR entities)
Autonomous research agents (e.g., paper summarization → code generation → experimentation)

The next wave of AI products will be agent-first, where users delegate problems to agents and receive end-to-end results—moving from prompt engineering to workflow delegation.

Conclusion

In Part 2 of the Agentic AI Interview Series, we explored advanced concepts like hierarchical planning, scratchpad reasoning, autonomous loops, safety, and evaluation. These questions reflect the real-world design and deployment challenges faced by teams building next-generation AI assistants and workflows.

Stay tuned for Part 3, where we’ll cover:

Memory persistence frameworks
Custom simulators for testing agents
Agent evaluation benchmarks
Scalable agent orchestration tools (e.g., CrewAI, LangGraph)
Agent alignment and user preference modeling

Resources

Langgraph