What should you focus on for an Agentic AI interview?
Can you explain what an agent is and when to use one?
Start with definitions and use cases. This topic appeared in 55 of 181 interviews. The most asked question covered what an agent is, its core components, and when to use one. Check that you can answer each part, not just give a definition.
- How does an agent differ from a chatbot, a RAG application, and a fixed workflow?
- What are its core components, and what does each do?
- When would you choose an agent, and what business value would it offer?
Can you explain your agent design choices?
In these interviews, 62% of questions were labelled senior level. Check whether you can explain a design, not just name a framework.
- How would you break a task into a workflow?
- Where do planning and state changes fit, and how would you coordinate the work?
- How would agents work together?
- When would you choose LangGraph over LangChain?
Use the application questions to check your design choices for voice, sales, ticket handling, testing, or browser automation tasks. Can you explain the whole design for one of these uses?
Can you trace a tool call and explain MCP?
Tool calling and MCP appeared in 40 of 181 interviews. Can you trace the path from selecting a tool to producing a final answer?
- Who selects the tool, who executes it, and how are the results used?
- What does an MCP server do, and how does it connect agents to tools and live data?
- How does MCP compare with function calling and agent-to-agent protocols?
A coding task appeared in 1 of 181 interviews: building an MCP server in TypeScript, including initialization, capabilities, tools, and prompts.
Can you manage instructions, context, and memory?
Check your answers about both agent instructions and the information an agent uses.
- Can you explain system prompts, expected output formats, and reusable skills in coding agents?
- How would you change a prompt based on observed results?
- How would you retrieve relevant evidence, check data freshness, and handle context limits?
- What memory persists, and how is conversation state managed?
Can you test and run an agent safely?
Check whether your answer covers testing, failures, and controls on agent actions.
- Testing: Which metrics and benchmark tasks would you use? How would you repeat tests and catch new failures after changes?
- Production: How would you debug failures, prevent loops, and manage latency and cost?
- Security: How would you set permissions, protect secrets, validate actions, and use human approvals?
Who gets asked Agentic AI
The 20 most asked Agentic AI questions
Counted by interview, not by mention: a question asked twice in one conversation counts once.
What is an AI agent, what are its core components, and when would you use one?
Asked in 10 of 181 interviews · last asked Oct 19 · Concept · Mid-level
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An AI agent is a system that chooses and takes actions to reach a goal, using feedback to decide what to do next. Its core parts are a decision-making model, instructions, task state, and ways to observe and act, often through tools. I use one when steps depend on changing results; fixed workflows are simpler for predictable tasks.
- Agents repeat an observe, decide, and act loop until they finish or reach a limit.
- Tools let agents search, run code, or call other systems; task state tracks progress.
- Use agents for changing, multi-step tasks, while weighing cost, delay, and reliability.
- Limit permissions and require human approval for high-risk actions.
What the interviewer checks: The interviewer is checking whether you understand the agent loop, its main parts, and when added autonomy is useful.
Other ways it was asked
- “What is an AI agent and how does it work?”
- “When should you use an AI agent?”
- “What are AI agent skills, and when would you use them?”
What is MCP, and how does it connect AI agents to tools and live data?
Asked in 8 of 181 interviews · last asked Sep 18 · Tooling · Mid-level
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MCP, the Model Context Protocol, is an open standard that connects AI apps to tools and data sources. The app uses an MCP client to connect to servers that expose tools, data resources, and prompt templates. Agents can use these connections to query live data or take actions without a custom connection format for every service.
- The client discovers available tools and sends tool calls to the server.
- Servers can connect to databases, files, APIs, and business systems.
- Data is live only if the connected source provides current data.
- MCP standardizes communication; apps still need permissions and approval rules for sensitive actions.
What the interviewer checks: The interviewer checks whether you understand MCP’s client-server design, practical uses, and limits.
Other ways it was asked
- “What do you know about MCP, the Model Context Protocol, for tool use?”
- “What is the Model Context Protocol (MCP), and what problem does it solve?”
- “What is the Model Context Protocol (MCP), and what are its use cases?”
What is agentic AI?
Asked in 7 of 181 interviews · last asked Sep 24 · Concept · Junior
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Agentic AI is AI that works toward a goal by choosing actions, taking steps, and adjusting based on results. Generative AI creates content; agentic systems can use it to plan and act, often through tools. An AI agent is an individual system that acts; agentic AI describes this broader approach, using one or more agents.
- It chooses steps toward a goal rather than only answering a prompt.
- It uses feedback to decide what to do next.
- Agentic AI does not require multiple agents or complete independence from people.
- Permissions, limits, and human approval help control risky actions.
What the interviewer checks: The interviewer wants to see whether you distinguish generating content from taking goal-directed actions.
Other ways it was asked
- “What is the difference between agentic AI and generative AI?”
- “What is the basic difference between an AI agent and agentic AI?”
- “What is the difference between agentic AI and AI agents?”
How would you build an AI agent, and where would frameworks like LangGraph or MCP fit?
Asked in 7 of 181 interviews · last asked Sep 10 · Design · Mid-level
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I would define the task, choose a model and tools, and build a loop that selects actions, checks results, and stops. I would track state, limit tool permissions, and test failure cases. LangGraph can manage state, branching, and pauses for human approval. MCP is a protocol, not an agent framework; it standardizes connections to tools and data sources.
- Start with a simple workflow; use agent decisions only where needed.
- Set time, cost, and step limits, with clear error handling.
- Require human approval for risky actions and test task success.
- LangGraph manages execution; MCP connects tools and data. Neither is required.
What the interviewer checks: The interviewer checks whether you can design a safe, testable agent and distinguish orchestration from tool integration.
Other ways it was asked
- “How would you build an AI agent?”
- “What components are needed to build an AI agent?”
- “How would you design the high-level architecture of an AI agent?”
What are agent skills, and how do you define and use them in coding agents?
Asked in 6 of 181 interviews · last asked Oct 14 · Concept · Mid-level
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Agent skills are reusable packages of instructions for specific tasks. In Claude Code and Codex, you define them in a folder with a SKILL.md file, optionally adding scripts and reference files. The agent uses the skill’s description to decide when to load its instructions. You can also request a skill directly; exact setup depends on the agent.
- SKILL.md includes a name, description, and task instructions. The description should explain when to use the skill.
- Skills provide task guidance; tools perform actions such as reading files or running commands.
- General project instructions apply broadly; skills are loaded for relevant tasks.
- Skills do not grant extra permissions. Scripts still need appropriate access and safety checks.
Code--- name: add-tests description: Use when adding tests for new code or bug fixes. --- Read the project instructions and nearby tests. Use the existing test framework. Cover normal behavior and relevant edge cases. Run the relevant tests if permitted. Report results, including any tests not run.What the interviewer checks: The interviewer is checking whether you can define reusable task guidance and explain how a coding agent finds and uses it.
Other ways it was asked
- “What is a skill in an AI agent?”
- “What are skills in AI agents, and how are they used?”
- “What are Skills in Claude or Codex?”
How do you evaluate an AI agent, and which metrics would you use?
Asked in 6 of 181 interviews · last asked Oct 16 · Concept · Senior
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I evaluate an AI agent on realistic tasks with clear success criteria, then monitor it in production. I measure task success, output quality, tool-use correctness, safety violations, latency, and cost per task. I also test failure recovery and repeat tasks to check reliability, using human review where automated checks are not enough.
- Measure completed goals, not just fluent answers.
- Inspect tool calls and intermediate actions, not only the final output.
- Test edge cases, unsafe requests, and tool failures.
- Compare success, cost, and latency against a baseline under the same conditions.
What the interviewer checks: The interviewer checks whether you can measure reliable task completion while balancing quality, safety, speed, and cost.
Other ways it was asked
- “How do you evaluate an AI agent?”
- “Which metrics would you use to measure an AI agent's effectiveness?”
- “Which metrics would you use to evaluate an AI agent?”
What is the difference between a chatbot, a RAG application, and an AI agent?
Asked in 5 of 181 interviews · last asked Oct 6 · Concept · Junior
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A chatbot is a conversational interface that responds to user messages. A RAG application retrieves information and uses it to help generate answers. An AI agent chooses and carries out steps toward a goal, often using tools and adjusting based on results. These can overlap: a chatbot can use RAG or provide access to an agent.
- Chatbot describes the interface; RAG describes how information is retrieved and used.
- Agents decide what to do next instead of only following a fixed sequence.
- Tool use alone does not make a system an agent.
- Agents can have limited permissions and require human approval for important actions.
What the interviewer checks: The interviewer is checking whether you distinguish conversation, retrieval, and goal-directed action without treating them as separate categories.
Other ways it was asked
- “What is the difference between a chatbot and an agent?”
- “What is the difference between a chatbot and an AI agent?”
- “What is an AI agent, and how does it differ from a chatbot interface?”
When would you choose LangGraph over LangChain?
Asked in 5 of 181 interviews · last asked Sep 27 · Concept · Senior
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I would choose LangGraph when I need explicit control over a stateful workflow, including branches, loops, and human approval steps. I would use LangChain for a faster start with standard agents, model integrations, and tools. They work together: LangChain’s current agent API is built on LangGraph.
- LangGraph lets you define steps, transitions, and shared state explicitly.
- Checkpoints let workflows resume from saved state, including after human review.
- LangChain agents already use LangGraph; choose LangGraph directly when you need more control.
What the interviewer checks: The interviewer is checking whether you can match workflow complexity to the right level of control.
Other ways it was asked
- “What is the difference between LangChain and LangGraph?”
- “When would you use LangChain, and when would you use LangGraph?”
- “When would you choose LangGraph over LangChain for an LLM application?”
What is an MCP server?
Asked in 5 of 181 interviews · last asked Oct 9 · Concept · Junior
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An MCP server is a program that exposes tools, data, or reusable prompts through the Model Context Protocol. An AI application connects to it using an MCP client. This lets the application access external systems in a standard way, such as reading files or calling an API.
- MCP stands for Model Context Protocol.
- Servers can expose tools, resources, and prompts; they do not need to provide all three.
- Servers can run locally or remotely.
- The AI application controls access and tool use; the server does not replace the model.
What the interviewer checks: The interviewer is checking whether you understand how MCP connects AI applications to external tools and data.
Other ways it was asked
- “What does an MCP server do?”
- “What is an MCP server, or Model Context Protocol server?”
- “What are MCP servers, and how are they used in LLM applications?”
How does an agent select tools, who executes them, and how do results become a final answer?
Asked in 3 of 181 interviews · last asked Oct 21 · Concept · Mid-level
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The app sends the model the user request and tool descriptions, including input schemas. The model chooses a tool based on the task and returns its name and arguments. The app checks the request, runs the tool, and sends its result back to the model. The model uses the result to answer or request more tools.
- Tool calling means requesting a function call; the model does not execute the function itself.
- The app validates arguments and checks permissions before execution.
- Tool results or errors go back with the matching call identifier.
- The model may answer without tools, or use several calls before answering.
What the interviewer checks: The interviewer checks whether you understand the tool loop and separate model decisions from app execution.
Other ways it was asked
- “How do agents choose and invoke tools internally?”
- “How do tools work in an LLM agent, and who is responsible for calling them?”
- “What is tool calling/function calling? Walk us through the four steps from when your code sends a request to when the user gets a final answer.”
How would you manage growing conversation history to keep an agent within its context limit?
Asked in 3 of 181 interviews · last asked Oct 6 · Design · Senior
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I would keep key instructions, recent messages, and a short summary of older turns. I would track token use and update the summary before reaching a budget that leaves room for tool results and the model’s reply. I would save full history outside the prompt, retrieve relevant details when needed, and drop repeated or irrelevant content first.
- Keep the current goal, user constraints, decisions, and unfinished work.
- Trigger summaries by token use, not just message count.
- Keep tool calls with their matching results when trimming history.
- Summaries can lose details; keep original records available for lookup.
What the interviewer checks: The interviewer is checking whether you can control context size without losing information needed to finish the task.
Other ways it was asked
- “How would you manage context in a long-running chat with a user?”
- “How would you manage context in a long-running chat, including recent messages, summaries, and when to generate a summary?”
- “In a multi-turn agent, how would you keep the conversation useful as history grows, and how would you decide what to drop?”
How would you assemble relevant data and context for an AI agent's prompts?
Asked in 3 of 181 interviews · last asked Oct 21 · Scenario · Mid-level
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I would build each prompt from the agent’s instructions, the user’s task, relevant conversation history, and retrieved data. Data could come from approved documents, databases, APIs, or previous tool results. Developers set access and selection rules; retrieval logic and, when allowed, the agent choose what fits the task and the prompt’s size limit.
- Retrieve only relevant data that the user is allowed to access.
- Keep source references and timestamps so the agent can check facts and freshness.
- Treat retrieved content as data, not instructions that can override the agent’s rules.
- Remove duplicates and summarize older history without losing important task details.
What the interviewer checks: The interviewer checks whether you can select useful context, explain who controls it, and respect access and size limits.
Other ways it was asked
- “How is context assembled for the assistant prompts? What information is included, where does it come from, and who decides what gets included?”
- “How would you provide an AI agent with the context it needs?”
- “How would you provide an AI agent with relevant data sources and context for a task?”
What distinguishes an AI agent from a fixed workflow?
Asked in 2 of 181 interviews · last asked Aug 7 · Concept · Senior
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An AI agent chooses its next action based on a goal and what it observes. A fixed workflow follows predefined steps and rules, even when it has branches. I would choose an agent when the steps cannot be fully planned in advance, and a fixed workflow when predictability and control matter more.
- Agents can adjust their actions based on new results or changing conditions.
- Fixed workflows can use AI, but their rules still control the path.
- Agents need clear limits and monitoring because their choices can be less predictable.
What the interviewer checks: The interviewer is checking whether you distinguish flexible action selection from following predefined rules.
Other ways it was asked
- “What distinguishes an AI agent from an autonomous workflow built from rigid rules?”
- “Why might you choose an AI agent rather than a fixed workflow?”
How does the ReAct agent pattern work, and how does it differ from plan-and-execute?
Asked in 2 of 181 interviews · last asked Jul 22 · Concept · Mid-level
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ReAct repeats a loop of reasoning, taking an action, and using the result to choose the next step. Plan-and-execute creates a plan first, then carries out its steps, with replanning when needed. ReAct adapts step by step, while plan-and-execute gives longer tasks more structure.
- ReAct uses tool results as feedback before choosing its next action.
- Plan-and-execute separates planning from execution; the plan does not have to stay fixed.
- ReAct can lose direction over many steps; an upfront plan can become outdated.
What the interviewer checks: The interviewer is checking whether you understand both control flows and when each is useful.
Other ways it was asked
- “What can you tell me about the ReAct pattern in the design of agentic systems?”
- “How do ReAct and plan-and-execute agent patterns differ?”
When would you use a fixed workflow, a single agent, or a multi-agent system?
Asked in 2 of 181 interviews · last asked Aug 13 · Concept · Senior
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I use a fixed workflow when the steps are known and predictable. I use a single agent when the task needs flexible decisions about steps or tools. I use multiple agents when separate roles or parallel work provide clear value. I start with the simplest option because more agents add cost and coordination risks.
- Fixed workflows are easier to test and control; they can still include AI calls.
- A single agent can choose tools and adapt its next step.
- Multiple agents suit separable tasks, but add delays, cost, and coordination failures.
- Use evaluations to check whether added complexity improves results.
What the interviewer checks: The interviewer is checking whether you match the design to the task rather than assuming more agents are better.
Other ways it was asked
- “Why might you build a fixed workflow when agent technology is available?”
How would you design a production multi-agent system end to end?
Asked in 2 of 181 interviews · last asked Oct 7 · Design · Senior
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I would start with the task, success metrics, and whether multiple agents are needed. I would give each agent a clear role and use an orchestrator to route work through explicit steps. I would define shared state, message formats, and tool permissions. Before launch, I would test failures, add monitoring and human approval, and roll out gradually.
- Use sequential steps for dependencies and parallel agents for independent work; define how conflicting results are resolved.
- Persist workflow state; use timeouts, bounded retries, and duplicate protection for actions with side effects.
- Limit tool access, validate outputs, and require human approval for high-risk actions.
- Evaluate end-to-end quality, trace agent decisions, and enforce cost, latency, and step limits.
What the interviewer checks: The interviewer is checking whether you can turn agent roles into a reliable, secure, and measurable production workflow.
Other ways it was asked
- “How would you orchestrate a multi-agent system?”
How do you choose between building custom agent tools and using existing APIs or native integrations?
Asked in 2 of 181 interviews · last asked Oct 14 · Design · Senior
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I use existing APIs or native integrations when they meet the task, security, and reliability needs. I build custom tools when I need missing features, special business rules, or tighter control. I compare setup effort, ongoing maintenance, cost, and vendor dependence. Often, a small custom wrapper around an existing API is enough.
- Check required features, permissions, rate limits, and error handling before choosing.
- Native integrations can save setup work, but may limit control or customization.
- Custom tools offer control but require testing, monitoring, and maintenance.
- Keep tool permissions narrow and require approval for sensitive actions.
What the interviewer checks: The interviewer is checking whether you balance task fit, security, and long-term maintenance instead of always building from scratch.
Other ways it was asked
- “How would you implement tool calling for an AI agent: build custom tools and invoke them through an API, or use native integrations such as Google Calendar?”
- “When building an AI agent, how do you decide whether to use existing tools or write custom tools?”
What are external tools in an AI agent, and how does MCP relate to them?
Asked in 2 of 181 interviews · last asked Sep 8 · Concept · Mid-level
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External tools are functions or services an AI agent can call, such as search, databases, or code runners. MCP stands for Model Context Protocol. It gives AI applications a standard way to discover and call tools exposed by MCP servers. I would use it to reuse tool connections across compatible applications; direct API calls are also an option.
- Tools let agents get current data or take actions beyond generating text.
- An MCP client connects to a server that exposes tools and describes their inputs.
- MCP standardizes the connection; it does not replace the underlying APIs or decide which tool to use.
- Tool access still needs permissions, input checks, and approval for risky actions.
What the interviewer checks: The interviewer checks whether you understand tools, MCP’s role, and when a standard connection is useful.
Other ways it was asked
- “What is MCP, and when would you use it to connect an AI agent to tools?”
How would you build an MCP server, and what components would it include?
Asked in 2 of 181 interviews · last asked Sep 8 · Design · Mid-level
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I would build an MCP server with an official SDK, tool definitions, and handlers that call application services. The AI application would host an MCP client that discovers and calls those tools. I would use stdio for a local process or Streamable HTTP for a remote server. I would add input validation, access checks, error handling, and logs.
- The SDK handles protocol messages, initialization, and capability negotiation.
- Expose three tools: search documents, fetch a document, and create a support ticket.
- Resources provide readable context; prompts provide reusable message templates. Both are optional.
- Keep credentials on the server; require user approval for sensitive changes in the client application.
What the interviewer checks: The interviewer checks whether you understand MCP’s client-server design and can expose useful capabilities safely.
Other ways it was asked
- “How would you architect an application using an MCP server, including the client-side components?”
- “How would you design an MCP server for an application, and what three tools or capabilities would it expose?”
How would you design an agent's system prompt, including instructions, output format, and quality standards?
Asked in 2 of 181 interviews · last asked Oct 21 · Concept · Mid-level
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I would define the agent’s role, goal, limits, tool rules, and when to ask for help. I would specify the output structure and quality rules, including accuracy, relevance, and how to handle uncertainty. I would version the prompt and test it on normal, unclear, and unsafe requests.
- Define allowed actions, tool use, and when human approval is needed.
- Specify required output fields and validate them in code; prompting alone does not guarantee compliance.
- Require supported claims, clear uncertainty, and no invented facts.
- Treat retrieved content as data, not instructions; enforce permissions outside the prompt.
What the interviewer checks: The interviewer is checking whether you can turn an agent’s goals into clear, testable instructions without relying on prompting alone.
Other ways it was asked
- “How would you design and manage the system prompt for an LLM agent?”
- “What information should an effective prompt for an AI agent include?”
- “How should an AI-agent prompt specify the output format and quality standards?”
Agentic AI basics and use case interview questions
Start with agent definitions, comparisons with chatbots and fixed workflows, use case choices, business value, and enterprise adoption. 52 questions.
A customer wants an AI agent for every workflow process, and a BI lead proposes replacing citizen developers’ Power BI and Tableau reports with a broader solution. How would you break the request into a scoped engagement?
Asked once · last asked Apr 20 · Scenario · Senior
When would you use an AI agent instead of building a complete application?
Asked once · last asked May 3 · Scenario · Senior
What factors do you consider when evaluating AI solutions, including agentic AI, AI governance, cloud workloads, and API integrations?
Asked once · last asked May 5 · Concept · Senior
Which go-to-market or revenue processes are especially well suited to AI or agent-based automation, and where are human judgment and collaboration still critical?
Asked once · last asked May 26 · Scenario · Senior
How does a custom AI agent for generating detailed designs and test cases differ from uploading a design document to ChatGPT?
Asked once · last asked Jun 25 · Concept · Mid-level
How do you turn a business need into an AI agent that addresses it, and how do you decide what to automate?
Asked once · last asked Jul 16 · Scenario · Senior
What are the challenges in existing agentic AI systems?
Asked once · last asked Jul 16 · Concept · Senior
What are agentic solutions, and when would you use them?
Asked once · last asked Jul 17 · Concept · Senior
How do agentic AI, multi-agent systems, and RAG architectures work, and when would you use them?
Asked once · last asked Jul 25 · Concept · Senior
How do AI agents and DAG-based systems work, and when would you use each?
Asked once · last asked Aug 4 · Concept · Mid-level
How are prompt engineering, RAG, spec-driven development, and agent orchestration used in AI applications?
Asked once · last asked Aug 5 · Concept · Mid-level
In what real-world scenarios would you use CrewAI, and why?
Asked once · last asked Aug 7 · Scenario · Mid-level
AI agent architecture and multi-agent interview questions
Explore agent components, planning, workflow design, orchestration frameworks, state changes, and how agents work together. 61 questions.
What information would you store in a LangGraph state object?
Asked once · last asked Jul 20 · Design · Mid-level
Other ways it was asked
- “How does LangGraph pass information between nodes, and what does the state look like?”
If you want to set up a new multi-agent assistant system for question answering and task execution, what tools would you use across the toolchain?
Asked once · last asked Oct 2 · Design · Senior
Other ways it was asked
- “What tools would you use across the toolchain to build a multi-agent assistant system?”
If you were designing an enterprise-grade agentic system from scratch today, what major architectural decisions would you need to make? Talk me through how you would approach the problem and the aspects you would cover.
Asked once · last asked Apr 17 · Design · Senior
How do Google ADK and the OpenAI Agent SDK compare for building agentic systems?
Asked once · last asked Apr 17 · Concept · Senior
How would you design an agentic AI workflow with LangChain, CrewAI, or a similar framework, covering its use case, orchestration, tool usage, memory, and reliability?
Asked once · last asked May 27 · Design · Senior
Other ways it was asked
- “Describe an agentic AI workflow you built with LangChain, CrewAI, or a similar framework, including its use case, orchestration, tool usage, memory, and reliability.”
What is the Agent node in n8n?
Asked once · last asked Jun 17 · Tooling · Mid-level
What are several design patterns for building multi-step agentic systems?
Asked once · last asked Jun 23 · Design · Senior
How do you choose between a low-code option and custom code for a node in an AI agent?
Asked once · last asked Jul 16 · Scenario · Senior
If you had to build an agentic AI system, what would be your plan? How would you design this system?
Asked once · last asked Jul 16 · Design · Senior
What is reflection in agentic AI?
Asked once · last asked Jul 16 · Concept · Mid-level
What are self-correction and refinement in agentic AI?
Asked once · last asked Jul 16 · Concept · Mid-level
What might a LangGraph flow look like, and why choose LangGraph over plain tool calling?
Asked once · last asked Jul 20 · Design · Mid-level
AI agent tool calling and MCP interview questions
These questions cover tool selection and execution, API connections, MCP servers and transports, function calling, and agent-to-agent protocols. 43 questions.
How does an MCP agent select a forecasting model?
Asked once · last asked Aug 24 · Concept · Senior
When is MCP useful, and when would you use it?
Asked once · last asked Feb 5 · Concept · Mid-level
How would you retrieve the dynamic data from the live database, including through MCP?
Asked once · last asked Feb 5 · Scenario · Mid-level
Can you explain what plugins are in Semantic Kernel?
Asked once · last asked Apr 24 · Concept · Mid-level
What kinds of tools can an AI agent call?
Asked once · last asked Apr 29 · Concept · Mid-level
How would you integrate LinkedIn or a similar platform with an LLM?
Asked once · last asked Jul 17 · Scenario · Mid-level
In a scenario with 10,000+ tools, how would you handle tool selection when you cannot list all tool descriptions in the prompt?
Asked once · last asked Jul 17 · Design · Senior
What are the benefits of using MCP compared with LangGraph?
Asked once · last asked Jul 20 · Concept · Mid-level
When might you choose not to use the Model Context Protocol (MCP) with Figma in a software development workflow?
Asked once · last asked Jul 28 · Concept · Mid-level
How do you invoke a planner agent through an API?
Asked once · last asked Jul 31 · Scenario · Mid-level
What advantages does an MCP server offer over a conventional server or regular backend logic?
Asked once · last asked Aug 4 · Concept · Mid-level
How does calling an API differ from interacting with an MCP server?
Asked once · last asked Aug 4 · Concept · Mid-level
AI agent prompting and skills interview questions
Focus on system prompts, instructions, expected output formats, reusable skills, coding agents, and prompt changes based on results. 20 questions.
Write a prompt for a multilingual voice AI agent with agentic tool orchestration.
Asked once · last asked Jul 27 · Mid-level
Other ways it was asked
- “How would you design and implement a multilingual voice AI agent with agentic tool orchestration?”
How do you use prompt engineering with LLMs, and what are the key steps in building an AI agent?
Asked once · last asked Feb 17 · Concept
How would you prompt a model to use reasoning or the ReAct approach?
Asked once · last asked Jul 16 · Scenario · Mid-level
How do you use agentic coding in your day to day, what tools do you use, and what works well or poorly for you?
Asked once · last asked Jul 24 · Tooling · Mid-level
How would you create and review skills, playbooks, or saved workflows for an internal AI assistant to ensure they are used effectively?
Asked once · last asked Aug 22 · Design · Senior
How would you implement prompt constraints and domain rules in an AI agent system?
Asked once · last asked Aug 26 · Scenario · Mid-level
How would you add dynamic prompt templates that evolve over time to an AI orchestrator?
Asked once · last asked Sep 24 · Design · Mid-level
When should you move a rule into a separate custom skill for an AI agent?
Asked once · last asked Oct 2 · Concept · Senior
How does a model's behavior differ when an instruction is provided as a rule rather than as a skill?
Asked once · last asked Oct 2 · Concept · Senior
Where would you store prompts and tool definitions for AI agents?
Asked once · last asked Oct 8 · Design · Mid-level
What standards do you follow when working with AI agents, and how do you configure them for a project?
Asked once · last asked Oct 19 · Tooling · Senior
How can you identify important omissions in an AI-agent prompt?
Asked once · last asked Oct 21 · Scenario · Mid-level
Show 6 more
How would you use findings from successive agent runs to identify remaining issues and improve the next iteration?
Asked once · last asked Oct 21 · Scenario · Mid-level
Other ways it was asked
- “How would you use findings from an initial AI-agent run to improve the next iteration?”
- “After revising an AI agent between runs, how would you identify and address remaining issues?”
Which AI development tools do you use day to day, and which actions can they take rather than merely suggesting code?
Asked once · last asked Sep 8 · Tooling
How do you use AI tools and agentic workflows when building software and writing scripts?
Asked once · last asked Oct 1 · Scenario · Senior
Other ways it was asked
- “Given the availability of AI and agentic workflows now, what's been your approach to building and scripting?”
- “How have you adapted your scripting work to account for AI and agentic workflows?”
What is a system instruction in an AI agent?
Asked once · last asked Jun 9 · Concept · Junior
How do you use AI agents to design and build solutions?
Asked once · last asked Jul 23 · Scenario · Mid-level
How would you write a prompt constraint for a specific AI agent use case?
Asked once · last asked Aug 26 · Scenario · Mid-level
AI agent memory, context management, and RAG interview questions
Work through context assembly, retrieval, fresh data, conversation state, stored memory, context limits, and evidence for agent answers. 40 questions.
How would you design an agent's memory and decide what conversation information to retain across sessions?
Asked in 2 of 181 interviews · last asked Aug 22 · Design · Senior
Other ways it was asked
- “How do you design memory for an agent?”
- “Can you explain whether the assistant can remember past conversations? If so, what information is retained or persisted across sessions?”
What is the difference between agentic RAG and custom RAG, and when would you use each approach?
Asked once · last asked Jul 23 · Concept · Senior
Other ways it was asked
- “How does custom RAG differ from agentic RAG?”
- “What factors determine whether to use custom RAG or agentic RAG?”
What information should an AI phone agent have to handle each insurance call effectively?
Asked once · last asked Jul 16 · Design · Senior
What information would you store from past tickets in the memory of a ticket-handling agent?
Asked once · last asked Aug 7 · Design · Senior
Other ways it was asked
- “What metadata would you store for past tickets in an agent's memory?”
What data would you store in the vector database for a ticket-handling agent?
Asked once · last asked Aug 7 · Design · Mid-level
Other ways it was asked
- “What data would you store in a conventional database for a ticket-handling agent?”
How would you decide which source to trust when tools return conflicting information?
Asked once · last asked Sep 22 · Scenario · Senior
How would you handle an agent that uses both static company-policy information and dynamic patient or insurance data, and where would fine-tuning fit?
Asked once · last asked Feb 5 · Design · Senior
How would you securely extract and evaluate information from a large PDF corpus for AI agents, comparing AWS or GCP tools, Palantir Foundry, and private cloud to right-size the architecture for a customer?
Asked once · last asked Apr 20 · Design · Senior
Other ways it was asked
- “How would you securely and accurately extract information from a large PDF corpus, evaluate the results, and make the information available to AI agents? How would you compare AWS or GCP tools, Palantir Foundry, and private cloud when right-sizing the architecture for a customer?”
How do you maintain conversation state in an AI agent application?
Asked once · last asked Apr 29 · Scenario · Mid-level
How does a resume-screening agent use the RAG architecture?
Asked once · last asked Jun 9 · Design · Senior
If job descriptions change by position, how would you train the agent to match each resume to the correct job description at runtime?
Asked once · last asked Jun 9 · Design · Senior
How can an AI agent use documents or online sources containing job descriptions to match resumes to roles?
Asked once · last asked Jun 9 · Design · Mid-level
All 40 AI agent memory, context management, and RAG questions
AI agent system design interview questions
Apply agent design to voice, sales, ticket handling, testing, browser automation, recommendations, and other business tasks. 50 questions.
How would you design a real-time voice agent from audio input to spoken response, including latency and failures?
Asked in 2 of 181 interviews · last asked Sep 29 · Design · Senior
Other ways it was asked
- “Can you walk me through the end-to-end architecture of a real-time multimodal voice AI agent from microphone input to spoken response, including latency budgets and session degradation handling?”
- “How would you design a telephone agent end to end, from an incoming call through generating a response?”
How would you design an AI system to determine whether a site unit is ready for site acceptance testing, identify blockers, and provide the right contact?
Asked once · last asked Sep 29 · Design · Senior
Other ways it was asked
- “How would you design an AI system to assess a site unit's readiness for site acceptance testing?”
- “How would you design a real-time AI system to determine whether a site unit is ready for site acceptance testing, identify blockers, and provide the right contact?”
- “How would you build an AI system for a field engineer to check whether a site unit is ready for site acceptance testing?”
What components would an agent-based system need to convert test scenarios into Playwright tests?
Asked once · last asked Oct 8 · Design · Senior
Other ways it was asked
- “How would you build an agent to convert test scenarios into Playwright tests?”
- “How long might it take to build an agent that converts test scenarios into Playwright tests?”
- “If agents could connect to a test management tool and convert scenarios into Playwright tests in tens of person-days, what would be wrong with that approach—or might nothing be wrong?”
What should an AI phone agent do after dialing an insurer's 1-800 number and the call starts ringing?
Asked once · last asked Jul 16 · Scenario · Mid-level
How would you design the tools and data pipeline for an AI scorecard agent?
Asked once · last asked Jul 25 · Design · Senior
Other ways it was asked
- “What model and overall architecture would you use for an AI scorecard agent?”
How can you use an AI agent to speed up software testing?
Asked once · last asked Oct 22 · Scenario · Senior
Other ways it was asked
- “What challenges can arise when using an AI agent to speed up software testing?”
How would you build an intelligent recommendation agent, and what technologies would you use?
Asked once · last asked Apr 10 · Design · Mid-level
How would you integrate an AI chatbot with a booking system?
Asked once · last asked May 22 · Design · Mid-level
Why might a candidate-screening agent use semantic analysis to shortlist resumes based on skills and a job description?
Asked once · last asked Jun 9 · Concept · Mid-level
How would you design an AI agent that processes high-level design documents and generates detailed designs and test cases, beyond relying on prompts and templates?
Asked once · last asked Jun 25 · Design · Senior
How would an AI sales agent trained on a retailer's products work?
Asked once · last asked Jul 16 · Scenario · Mid-level
If a customer is unsure between two products, how should an AI sales agent help them decide?
Asked once · last asked Jul 16 · Scenario · Mid-level
AI agent evaluation and testing interview questions
Check your understanding of metrics, scoring guides, benchmark tasks, repeatable tests, failures after changes, human review, and business impact experiments. 39 questions.
When designing a benchmark for an AI agent, what are the essential components of a good rubric?
Asked in 2 of 181 interviews · last asked Aug 29 · Scenario · Senior
Other ways it was asked
- “When you design an evaluation rubric for an AI agent, what are the minimum required rubric components to make scoring consistent and auditable?”
How would you build an automated evaluation harness to catch regressions before deploying model or prompt changes?
Asked in 2 of 181 interviews · last asked Oct 13 · Scenario · Senior
Other ways it was asked
- “How would you build a programmatic evaluation harness using benchmark test suites or deterministic text fixtures to measure an agent's accuracy before deploying updates?”
- “How would you set up evaluations for AI agents to detect regressions when changing the model or prompts?”
What common challenges do you face when designing evaluation frameworks for AI agents, and how do you address them?
Asked once · last asked Apr 27 · Scenario · Senior
In an agentic AI application, how can you evaluate whether an LLM is behaving as expected and providing correct answers?
Asked once · last asked Apr 29 · Scenario · Senior
When you turn fragmented incident inputs—logs, chats, dashboards, and tickets—into an agent evaluation workflow, what core steps and artifacts do you require before judging the agent's performance?
Asked once · last asked Jun 15 · Scenario · Senior
How would you define dependencies, blockers, and unknowns in an agent evaluation workflow so the agent can proceed safely without guessing while still making progress?
Asked once · last asked Jun 15 · Scenario · Senior
How would you score and weight an agent's performance across timeline accuracy, dependency handling, escalation behavior, and recommendation quality without over-optimizing for speed?
Asked once · last asked Jun 15 · Scenario · Senior
How would you use feedback to help an AI agent avoid repeating incorrect results?
Asked once · last asked Jun 25 · Scenario · Senior
Suppose Goodhart’s law takes effect and agents start optimizing for rubric artifacts, such as formatting and verbosity, without real task success. How would you redesign the rubric and success criteria to resist gaming?
Asked once · last asked Jun 30 · Scenario · Senior
Given a ten-minute observation session of an AI agent, what exact template section headers would you use to document behavior and outcomes, and what one or two sentences would you include per section?
Asked once · last asked Jun 30 · Mid-level
How do you measure an AI agent's error rate with human-in-the-loop review, and use the results to reduce hallucinations?
Asked once · last asked Jul 16 · Scenario · Senior
How would you ensure that shipping AI agents improves the relevant success metrics?
Asked once · last asked Jul 25 · Scenario · Mid-level
Other ways it was asked
- “How do you balance the requirement that metrics improve with shipping AI agents?”
Show 26 more
What constraints should you account for when testing an AI agent?
Asked once · last asked Jul 30 · Scenario · Senior
What would you do if the success metric for a ticket-handling agent were to reduce the number of tickets in the human queue?
Asked once · last asked Aug 7 · Scenario · Senior
How could a metric focused on reducing tickets in the human queue be gamed?
Asked once · last asked Aug 7 · Scenario · Senior
How would you approach writing and maintaining tests for an AI agent?
Asked once · last asked Aug 7 · Tooling · Mid-level
How would you incorporate user feedback into the ticket-handling agent system?
Asked once · last asked Aug 7 · Design · Senior
How would you assess an agentic AI solution before and after deployment, including its evaluation harness, data integration challenges, metrics, and alignment with business goals?
Asked once · last asked Aug 19 · Design · Senior
How would you evaluate agents pragmatically when models, harnesses, and MCP tools are changing constantly?
Asked once · last asked Aug 20 · Scenario · Senior
How would you use an evaluation framework to compare model versions or configurations side by side in an agent harness?
Asked once · last asked Aug 20 · Scenario · Senior
How would you evaluate autonomous agents, given that execution is nondeterministic?
Asked once · last asked Aug 25 · Scenario · Senior
What specific metrics beyond the classic NLP metrics would you set up to measure multi-step planning accuracy?
Asked once · last asked Aug 25 · Scenario · Senior
What is the difference between model-level metrics and system-level agent evaluation?
Asked once · last asked Aug 25 · Concept · Mid-level
How would you design a self-contained evaluation task for an agent doing a practical computer workflow, such as cleaning a CSV and producing a summary table, including inputs, oracles or validators, and a mostly automatable rubric?
Asked once · last asked Aug 29 · Scenario · Senior
How would you measure whether the classifier is actually improving routing and reducing unnecessary tool calls?
Asked once · last asked Sep 22 · Scenario · Senior
What metrics should you track to know if the routing and caching optimizations are working?
Asked once · last asked Sep 22 · Scenario · Senior
How do you set up the task for your AI agent and how do you verify the outcome?
Asked once · last asked Sep 24 · Tooling · Mid-level
How would you set up a single run to elicit both compliance handling and evidence-backed field validation, including the inputs, constraints, agent actions, and required artifacts?
Asked once · last asked Sep 26 · Scenario · Senior
How would you design a three- to four-week experiment to establish the causal effect of MCP on retention? Specify the primary metric, guardrails, and clear thresholds for success and failure.
Asked once · last asked Oct 2 · Scenario · Senior
How would you structure the test matrix across agents, versions, settings, and seeds to make comparisons fair and reproducible?
Asked once · last asked Oct 5 · Scenario · Senior
If the same prompt produces different failures across five runs, how would you document those failures and assess the agent's reliability?
Asked once · last asked Oct 5 · Scenario · Senior
How would you distinguish model nondeterminism from test harness flakiness, network rate limits, or tool timeouts when failures occur intermittently across agents?
Asked once · last asked Oct 5 · Scenario · Senior
Would you use a fixed test suite or create new tests each time to check whether an AI agent's guardrails trigger correctly?
Asked once · last asked Oct 13 · Scenario · Senior
What are the most important issues to look for in an AI agent's first output?
Asked once · last asked Oct 21 · Scenario · Mid-level
What final step would you take before deploying a ticket-handling agent to users?
Asked once · last asked Aug 7 · Scenario · Mid-level
Other ways it was asked
- “What questions would you ask users after deploying a ticket-handling agent?”
Should test-case creation be included in the technical specification for an AI agent?
Asked once · last asked Aug 7 · Concept · Mid-level
How do you measure return on investment for AI agents?
Asked once · last asked Aug 19 · Concept · Mid-level
Other ways it was asked
- “How do you measure ROI for agentic AI systems in insurance, and how would you explain to a client whether the investment is paying off?”
What kinds of harnesses can be used to run or evaluate AI agents?
Asked once · last asked Aug 26 · Concept · Mid-level
AI agent production deployment and reliability interview questions
Questions here cover deployment, releases, monitoring, debugging, failure recovery, loop prevention, latency, cost, and scaling. 40 questions.
How would you deploy an AI agent application, and what production stack would you use?
Asked in 2 of 181 interviews · last asked Jun 17 · Scenario · Mid-level
Other ways it was asked
- “How would you deploy an AI agent application?”
- “What technology stack would you use to deploy AI agents in production?”
What causes agents and tools to loop, and how would you define safe termination conditions?
Asked in 2 of 181 interviews · last asked Oct 21 · Scenario · Senior
Other ways it was asked
- “How would you prevent a tool from getting stuck in an infinite loop in an LLM agent?”
- “What are five distinct causes of agent loops, and how would you define termination conditions that prevent both infinite loops and premature termination?”
What optimizations would you make to reduce tool calls or scale the tool execution pipeline?
Asked once · last asked Sep 22 · Design · Senior
Other ways it was asked
- “What optimizations would you implement to reduce tool call costs?”
What telemetry and logs would you collect to monitor AI agents and understand their actions?
Asked once · last asked Oct 8 · Scenario · Senior
Other ways it was asked
- “How would you track AI agents and the actions they perform?”
- “How would you track more than 700 AI agents and the actions they perform?”
- “What telemetry would you use to monitor AI agents?”
What design patterns would you use to scale an AI agent to 10,000 users?
Asked once · last asked Apr 17 · Design · Senior
What change and release process would you use to deploy AI agents to production, including CI/CD pipelines and required approvals?
Asked once · last asked May 13 · Scenario · Senior
What techniques can you use to reduce latency and memory usage in models and AI agents?
Asked once · last asked Jul 16 · Concept · Senior
How would you diagnose the cause of a connectivity issue affecting an AI agent?
Asked once · last asked Jul 17 · Scenario · Mid-level
What could go wrong if you changed an AI prompt but not the worker code that enforces the same rule?
Asked once · last asked Jul 26 · Scenario · Senior
What is tracing for AI agents, and when would you use it?
Asked once · last asked Jul 28 · Concept · Mid-level
What architectural components are needed to make an AI agent system production-ready?
Asked once · last asked Aug 7 · Design · Senior
Other ways it was asked
- “What architectural components would be missing from an AI agent system before it is ready for production?”
What is a difficult technical problem you have encountered in an AI agent system, such as tool calling, data consistency, latency, or hallucinations, and how did you diagnose and fix it?
Asked once · last asked Aug 16 · Behavioral · Mid-level
All 40 AI agent production deployment and reliability questions
AI agent security, guardrails, and governance interview questions
Review identity, permissions, secrets, prompt injection, privacy, action checks, human approvals, ownership, audit records, and incident response. 50 questions.
How would you verify an AI agent's SQL read and write actions before allowing them to reach production?
Asked once · last asked Sep 8 · Scenario · Senior
Other ways it was asked
- “How would you verify an AI agent's SQL read and write actions before production, for example with dry-run transactions or approval gates?”
How would you design human-in-the-loop governance for an agentic system, including where to draw the line between autonomous action and human involvement?
Asked once · last asked Apr 17 · Scenario · Senior
Other ways it was asked
- “How would you think about governance, particularly human-in-the-loop governance, in an agentic system? Where would you draw the line between autonomous action and involving a human?”
How would you provide governed access to data and tools for generative AI and agentic workloads using Amazon Bedrock AgentCore, and what role could MCP servers play?
Asked once · last asked Jun 16 · Design · Senior
How do you store secrets and keep them secure in AI agents or applications?
Asked once · last asked Jul 16 · Scenario · Mid-level
Other ways it was asked
- “How do you provide secrets to an AI agent, and how does it retrieve them at runtime?”
- “How can an AI agent retrieve secrets securely without including them in its context?”
Why might you place a hard stop in an AI marketing agent after it generates campaign IDs, and what should happen next?
Asked once · last asked Jul 20 · Scenario · Senior
How can CyberArk support security for AI agents?
Asked once · last asked Jul 23 · Concept · Senior
How do authentication and authorization work from a user's request through MCP to tool execution?
Asked once · last asked Jul 25 · Concept · Senior
Other ways it was asked
- “How does authentication and authorization work in an MCP layer?”
- “How does authentication and authorization work when a cloud interface request calls a tool through an MCP endpoint?”
How would you design policy layers for an AI agent at the channel, product, and system levels, accounting for social media inputs, employee communications, and file structures?
Asked once · last asked Jul 27 · Design · Senior
How would you assess data privacy requirements and choose a deployment approach for an agentic workflow?
Asked once · last asked Jul 28 · Scenario · Senior
What risks arise when using n8n without hard contract enforcement, compared with GCP Agent Platform?
Asked once · last asked Aug 5 · Concept · Senior
What validation would you add to an AI agent system?
Asked once · last asked Aug 7 · Design · Senior
Where would you enforce guardrails for an access-granting agent: in the agent, the tool, or both?
Asked once · last asked Aug 7 · Design · Senior
All 50 AI agent security, guardrails, and governance questions
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How this list is made
Every question here was asked by a real interviewer in a real job interview that a candidate ran MeetAssist in. After the call, the interviewer's questions are pulled from the transcript. Practice sessions, tests, exams and anything that was not a job interview are dropped.
Questions are rewritten so they make sense on their own: names of people, employers and clients are removed, and speech-to-text mistakes are fixed. Small talk, logistics and questions about one candidate's own résumé are left out. Rephrasings of the same question are merged, and the count is the number of different interviews it came up in.
The interviews come from candidates who use MeetAssist, so they lean towards software, data and AI roles: the shares describe these interviews, not every Agentic AI interview. Answers to the most asked questions are written and checked by an editor; a "common mistake" is shown only when reviews of real answers in these interviews show it, with the count.
181 interviews, 116 candidates, Nov 2025 to Oct 2026. Updated every month with new interviews.
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