All 40 questions
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
Show answer
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
Show answer
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?”
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
How would you automatically make a newly uploaded job description available to an AI agent?
Asked once · last asked Jun 9 · Design · Senior
How would you prevent an AI sales agent from hallucinating when a customer asks about a product that does not exist?
Asked once · last asked Jul 16 · Scenario · Mid-level
What memory types does LangChain provide, and when would you use each?
Asked once · last asked Jul 17 · Concept · Mid-level
What is episodic memory, and how is it used in agent design?
Asked once · last asked Jul 22 · Concept · Senior
Do multiple AI agents run into context-window limitations, and how would you address them?
Asked once · last asked Jul 22 · Scenario · Senior
How would you determine a user's current stage in a chat conversation?
Asked once · last asked Jul 27 · Scenario · Mid-level
How would you retrieve relevant information from an AI agent's memory of past tickets?
Asked once · last asked Aug 7 · Design · Senior
How would you integrate shared company knowledge into an internal AI assistant, keep it up to date, and assign responsibility for its accuracy?
Asked once · last asked Aug 22 · Design · Senior
How do you design short-term versus long-term agent memory so it remains useful without causing context bloat or privacy issues?
Asked once · last asked Aug 24 · Design · Senior
How would you manage memory in multi-tenant agentic workflows?
Asked once · last asked Aug 25 · Design · Senior
How would you design short-term and working memory for an autonomous agent?
Asked once · last asked Aug 25 · Design · Senior
How would you manage context and domain rules across sessions in an AI agent system?
Asked once · last asked Aug 26 · Design · Senior
How can an LLM application customize an agent's prompt or context with user-specific information?
Asked once · last asked Sep 2 · Design · Senior
How do we manage the long-term and short-term memory of an AI agent?
Asked once · last asked Sep 7 · Design · Senior
What context would you provide to an AI agent, and how would you inject it?
Asked once · last asked Sep 8 · Behavioral · Mid-level
Other ways it was asked
- “What context did you provide to the AI agent, and how did you inject it?”
How can an agent determine whether its existing knowledge base contains an answer before performing a web search?
Asked once · last asked Sep 22 · Scenario · Senior
If staff wanted to ask an AI agent questions about farm benchmark results, how would you prepare and align the underlying data, and check that the answers were trustworthy and supported by the data?
Asked once · last asked Sep 23 · Design · Senior
How would you prevent hallucinations in an AI agent and keep its responses grounded in evidence?
Asked once · last asked Sep 25 · Design · Senior
Why might multi-agent system failures stem from context management rather than model capability?
Asked once · last asked Sep 28 · Concept · Senior
How do AI systems implement context recall?
Asked once · last asked Oct 1 · Concept · Senior
How would you improve a real-time RAG pipeline for AI agents?
Asked once · last asked Oct 7 · Design · Senior
How would you manage and test agent context to prevent a supervisor from becoming overloaded?
Asked once · last asked Oct 14 · Design · Senior
How would you identify context that was missing from an AI agent's initial run?
Asked once · last asked Oct 21 · Scenario · Mid-level
What's the difference between context compaction and reset? When would you use each?
Asked once · last asked Oct 21 · Concept · Senior
What do you know about RAG agents and Gemini?
Asked once · last asked Jul 16 · Concept · Mid-level
Can you explain what LLM and RAG are, and what two types of tokens are available in agentic AI?
Asked once · last asked Jul 16 · Concept · Junior
More Agentic AI interview questions
- All Agentic AI interview questions
- Agentic AI Basics and Use Case Interview Questions
- AI Agent Architecture and Multi-agent Interview Questions
- AI Agent Tool Calling and MCP Interview Questions
- AI Agent System Design Interview Questions
- AI Agent Production Deployment and Reliability Interview Questions
- AI Agent Security, Guardrails, and Governance Interview Questions
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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