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Read More →Practise the questions that separate real agent engineers from prompt-tuners. Learn to explain planning loops, tool calling, ReAct, multi-agent systems, MCP and safety guardrails — with structured sample answers.
What makes an AI agent different from a basic chatbot?
Interviewers commonly explore what makes a system an agent, tool or function calling, planning loops, memory, orchestration, evaluation and safety. Candidates should be able to explain how an agent works, when to use one, and how to control its behaviour.
Don't just define an agent. Explain how it decides, acts, observes and stops.
Educational sample questions written to cover common concepts. They are not questions verified to have been asked by any particular company.
An AI agent is a program that uses a language model to decide what to do next, calls tools such as search or APIs, observes the results, and repeats until the goal is met or it must stop. It adds tools, state, and a control loop to a basic model call.
Emphasise the loop and the difference from a single model response.
A chatbot responds to a message. A RAG app retrieves documents to support an answer. An agent can plan steps, choose tools, and act on results. The distinction is autonomy over multiple steps.
Compare behaviour, not implementation details.
ReAct alternates between reasoning and acting. The model decides, uses a tool, observes the result, and reasons again. This makes the action-observation cycle explicit and easier to inspect.
Tie it to why the loop must have an exit condition.
Multiple agents can help when a task naturally divides into specialised roles — research, writing, review. They also add coordination costs, so use them only when the benefit is clear.
Name the trade-off: specialisation vs coordination overhead.
Limit tool permissions to the minimum needed, cap execution steps and cost, validate inputs and outputs, require human approval for risky actions, log tool calls, and test realistic failure cases.
These are design safeguards — not a guarantee of safety.
The Model Context Protocol is an open standard for connecting AI applications to tools and data sources in a consistent way, so a tool can be exposed once and used by compatible applications.
Mention that a simplified diagram is fine in an interview.
Choose a question, form your answer mentally, then reveal the suggested response with key points and a follow-up.
What is an AI agent?
Form your answer before revealing. Think about the model, tools, state and control loop.
Three fictional engineering situations. Expand each to see suggested discussion points.
An agent repeatedly calls the same tool without making progress.
"How would you investigate and prevent this behaviour?"
An agent uses an unrelated tool even though a more suitable one is available.
"How would you improve reliability?"
A task contains research, drafting and review stages.
"When would you use multiple agents instead of one?"
Study material with sample questions and structured answers for revision. All three PDFs are free to download — no form or registration required.
Short answers to the questions candidates ask most often about Agentic AI interviews.
Strengthen your understanding of AI agents, orchestration and multi-step LLM applications through Uncodemy's learning resources and courses.
Explore CoursesThese PDFs contain educational material and sample interview questions. The questions are designed to cover common technical concepts; they are not represented as questions asked by a particular company. These resources are not certificates, degrees or promises of employment. Agentic AI tools and practices evolve, so consult official documentation for current details. Spot an error? Write to info@uncodemy.com.
