Agentic AI Interview Questions: What Companies Are Asking in 2026
Top agentic AI interview questions companies are asking in 2026. Concepts, frameworks, and how to prepare for AI engineering interviews.
AI engineering roles are now appearing at Indian startups and product companies. Here are the real questions being asked — and how to answer them.
What Is an Agentic AI System?
An agentic AI system is one that can autonomously plan and execute multi-step tasks using tools, memory, and reasoning. Unlike a simple chatbot that responds to one prompt, an agent can browse the web, write and run code, query databases, and take sequential actions toward a goal.
Conceptual Questions
Q: What is the difference between a chatbot and an AI agent?
A chatbot responds to a single prompt. An AI agent has a goal, plans steps to achieve it, uses tools, and acts autonomously across multiple steps.
Q: Explain the ReAct (Reason + Act) framework.
ReAct is a prompting pattern where the LLM alternates between reasoning (thinking about what to do) and acting (calling a tool). The result of the action feeds back into the next reasoning step.
Q: What is RAG (Retrieval-Augmented Generation)?
RAG enhances an LLM by retrieving relevant documents from a knowledge base (using vector search) and including them in the prompt context. This lets the LLM answer questions using your private/custom data.
Q: What is a vector database? Name some examples.
A vector database stores embeddings (numerical representations of text/images) and allows fast similarity search. Examples: Pinecone, Chroma, Weaviate, Qdrant, FAISS (local).
Q: What is prompt injection and how do you defend against it?
Prompt injection is when malicious user input manipulates the LLM to ignore its system prompt or perform unintended actions. Defense: input validation, output filtering, sandboxing tools, separate untrusted inputs from trusted instructions.
Q: Explain the difference between fine-tuning and RAG. When would you use each?
Fine-tuning: updates model weights on domain-specific data. Good when style, format, or deep domain knowledge needs to be baked in. RAG: retrieves context at inference time. Good for frequently updated data or when you need source attribution.
Q: What is LangChain? What problem does it solve?
LangChain is a framework for building LLM-powered applications. It abstracts common patterns: chaining prompts, connecting tools, managing memory, and building agents.
Q: What are function calling / tool use in LLMs?
The LLM can be given a list of available functions (with descriptions and schemas). When the LLM decides a function is needed, it returns a structured call instead of text. Your application executes the function and feeds the result back to the LLM.
Coding / Practical Questions
Q: Write code to build a simple RAG pipeline.
Key steps: load documents → split into chunks → embed chunks → store in vector DB → on query, embed query → retrieve top-k similar chunks → pass chunks + query to LLM → return answer.
Q: How would you evaluate an LLM application?
Using metrics like: faithfulness (does the answer use the retrieved context?), relevance (is the context retrieved relevant?), answer correctness (is the final answer right?). Tools: RAGAS, DeepEval, LangSmith.
Q: Design an AI agent that can answer questions about your company's internal docs.
Architecture: document ingestion → chunking → embedding → vector store → query interface → RAG pipeline → LLM for answer generation. Add: auth, audit logging, feedback loop.
Practical Resources
- LangChain documentation
- OpenAI function calling docs
- Hugging Face agents course
- Build and deploy 3 real projects before interviewing
About Satyaki Das & Classroom Tech
Satyaki Das is the Founder of Classroom Tech and Co-Founder of XShare. He holds an MTech in Computer Engineering from Jadavpur University and an MCA from Techno Main, Salt Lake, Kolkata. A Java Full Stack Developer at TCS, GATE-qualified, and Codevita Rank 848, Satyaki has 11+ years of teaching experience mentoring BCA, B.Tech, MCA, and working professionals into roles at TCS, Wipro, Infosys, Accenture, EY, Deloitte, and Capgemini.
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