Interview Questions HR
Agent Orchestration
1. The "What & Why" Question
Question: "What is the main difference between a simple LLM chain (like a basic chatbot) and an Agent Orchestration system?"
| What the Recruiter should listen for | Why it matters |
|---|---|
| "Autonomous" / "Agency" | Agents make their own decisions; chains follow a fixed path. |
| "Tool Calling" / "Function Calling" | Agents use external tools (Google Search, Python, APIs). |
| "Iterative" / "Loops" | Agents can go back and fix mistakes; chains usually move one way. |
2. The "Design Pattern" Question
Question: "Can you name a few common orchestration patterns or ways multiple agents work together?"
| What the Recruiter should listen for | Why it matters |
|---|---|
| "Hierarchical" (Manager-Worker) | One 'manager' agent assigns tasks to specialized 'worker' agents. |
| "Sequential" | Agent A finishes, then passes the result to Agent B. |
| "DAG" (Directed Acyclic Graph) | A technical way of describing a complex, mapped-out workflow. |
| "Joint Collaboration" / "Peer-to-peer" | Agents talking back and forth freely to solve a problem. |
3. The "State Management" Question
Question: "How do you handle 'State' or Memory when you have four or five different agents working on one project?"
| What the Recruiter should listen for | Why it matters |
|---|---|
| "Shared State" | A central 'blackboard' or database all agents can read from. |
| "Short-term vs. Long-term memory" | Knowing what happened 2 minutes ago vs. remembering user preferences. |
| "Persistence" / "Checkpoints" | Saving progress so the system can resume if it crashes. |
4. The "Reliability" Question
Question: "Agents are known for 'hallucinating' or getting stuck in loops. How do you implement error handling in orchestration?"
| What the Recruiter should listen for | Why it matters |
|---|---|
| "Self-Reflection" / "Critique" | One agent checks another agent’s work for mistakes. |
| "Human-in-the-loop" (HITL) | Pausing the agent to ask a human for approval before acting. |
| "Retries" / "Fallback" | If a tool fails, the agent tries a different approach or a simpler model. |
5. The "Tools of the Trade" Question
Question: "Which orchestration frameworks have you used, and why did you choose them over others?"
| What the Recruiter should listen for | Why it matters |
|---|---|
| "LangGraph" | Great for complex loops and very popular right now. |
| "CrewAI" | Good for role-playing agents (e.g., 'Researcher' and 'Writer'). |
| "AutoGen" | Microsoft’s framework; heavy on conversational agents. |
| "PydanticAI" / "Semantic Kernel" | Newer or enterprise-grade tools for stricter data control. |
Pro-Tip for the Recruiter: If a candidate mentions "Token Cost" or "Latency" without being asked, that’s a huge bonus. It shows they care about the company's budget and the actual user experience, not just the "cool" tech.
Others
- What is Retrieval-Augmented Generation (RAG), and what limitations of LLMs does it address?
- What is chunking in a RAG pipeline, and how does chunk size affect retrieval quality and model performance?
- What is hybrid search in RAG systems, and why is combining keyword and semantic search beneficial?
- What is the difference between extractive and abstractive summarization?
- What is Named Entity Recognition (NER), and what are some common entity categories used in practice?
- What is overfitting, and what techniques can be used to prevent it?
- Explain the bias–variance tradeoff and its impact on model performance.
- What is concept drift, and why is monitoring it important in production ML systems?
- Why is serving large language models more computationally expensive than traditional machine learning models?
- What is the ReAct pattern in AI agents, and how does it improve reasoning and decision-making?