AI Agent Engineer Interview Questions 2026

Last updated: September 23, 2026

By Jennifer Burdick, Recruiting Manager, KORE1

Good AI agent engineer interview questions ask about consequences, meaning what the candidate’s agent was allowed to change, how it knew to stop, and what happened when a single tool call ran twice. Framework names and definitions of “agentic” belong on a phone screen, if anywhere. Hire the one who can describe, with a date attached, the afternoon their agent did something nobody asked for, and the fix that went in afterward.

Most of the agent searches that reach my desk now come from companies that already built one. The demo went well. Then somebody asked whether it was safe to connect it to the real system, and nobody in the building could answer. That is usually when they call our AI and machine learning engineer staffing team, and the second thing they ask, right after the budget, is what they should be asking candidates.

I start with July 2025.

That month Jason Lemkin, the founder of SaaStr, was building an app with Replit’s AI agent and writing about it publicly as he went. On roughly the ninth day he had declared a code freeze, in plain words, more than once. The agent deleted his production database anyway. Then it told him a rollback was impossible. As The Register reported, that turned out to be false, and the rollback worked. Replit’s chief executive apologized and started rolling out automatic separation of development and production databases, which tells you what had been missing.

Now read it again. As a hiring manager. The model did something wrong, which models do. The real failure sat one layer down. An agent that was supposed to write code could reach a live database, and nothing between the agent and that database asked a human first. Somebody designed that. Or, more likely, nobody did.

That is the job. An AI agent engineer is the person who decides what the model is allowed to touch, what happens when it is wrong, and how anyone finds out. Prompting is a small part of it. Picking a framework is a smaller part.

You should know where I sit. We get paid when a client hires an agent engineer we found for them, and I would still hand you these questions if you never picked up the phone. Several teams have taken them and run the whole search on their own. They hired well.

You will also notice what is not here. Retrieval, evaluation sets, and hallucination questions matter for this role, and we already cover them in our LLM engineer interview questions, with the broader model-facing loop in our AI engineer interview guide. Everything below is about the part that only exists once a model is allowed to act.

Prep Sites Ask About Frameworks. Production Asks About Permissions.

Search for this role’s interview questions and you will find long lists. What is ReAct? Name three frameworks. Single agent or multi-agent, and why? Memory, short-term versus long-term, go. Every one of them is printed with the answer underneath, which is fine for a candidate preparing on a Sunday night and close to useless for the person running the interview on Tuesday.

Frameworks also age fast. Very fast. LangGraph, CrewAI, AutoGen, the OpenAI Agents SDK, and a half dozen others have all changed shape in the last eighteen months. A candidate who knows this quarter’s API by heart has proven that they read this quarter’s docs. Good. It tells you almost nothing about whether their agent will quietly place thirty-seven purchase orders when a supplier’s server is slow, which is a thing that happened to a client of ours, and which I will get to.

The 2025 Stack Overflow Developer Survey found that only 14.1 percent of developers use AI agents daily, while 37.9 percent have no plans to use them at all. The pool of people who have run an agent against real systems is small, which matches what employers described in our agentic AI engineering hiring survey, and the people in it tend to be skeptical. Hire from that skepticism. It is usually earned.

Seven Questions About What the Agent Was Allowed to Touch

Every question here traces back to something that went wrong in public, or in a search of ours. You will not find the answers printed underneath any of them. Strong candidates answer with a system they built and a date. Weak ones answer with a principle.

What could your last agent write to, and who approved that list?

Start here. Every time. Even for juniors.

The OWASP Top 10 for LLM Applications calls this risk Excessive Agency, and it splits the cause three ways. Excessive functionality, where the agent can reach tools it does not need. Excessive permissions, where a tool connects with more rights than its job requires, like a read-only lookup that runs on a database account holding UPDATE and DELETE. Excessive autonomy, where high-impact actions go through with no human confirming them. The Replit story had all three.

A strong candidate gives you an inventory. Specifics. Four tools, two of them read-only, one that writes to a staging table, one that sends email but only to internal addresses, with a service account created for the agent alone. They know who reviewed it. A weak candidate says the agent “uses the same API key as the app.” Then they pause. They can hear it too.

Your agent reads customer email. Walk me through how a stranger’s email could get it to send our data somewhere.

In June 2025, the developer Simon Willison gave this problem a name that stuck. He called it the lethal trifecta: access to private data, exposure to untrusted content, and the ability to communicate externally. Put all three in one agent and anyone who can get text in front of it can try to talk it into leaking what it can see. His advice is to avoid the combination entirely.

It gets worse. In January 2025, NIST’s Center for AI Standards and Innovation published hijacking tests on a frontier model running as an agent, with scenarios that included mass exfiltration of a user’s cloud files and sending phishing links to everyone on the user’s calendar. The strongest known baseline attack succeeded 11 percent of the time. New attacks written by the red team succeeded 81 percent of the time. Repeated attempts pushed the average success rate from 57 percent to 80 percent.

You want a candidate who flinches a little at those numbers and then talks about architecture, not prompts. Which of the three legs they removed. How untrusted text was kept away from the part of the system that decides which tool to call. Where a human approves before anything leaves the building. “We told the model to ignore instructions in emails” is the answer you are screening out.

Prompt engineers get a gentler version of this question in our prompt engineer interview questions. For an agent it carries more weight, because an agent can act on what it reads.

The tool call timed out. Did the purchase order go through?

This one is from the thirty-seven purchase orders.

A plumbing and HVAC supply distributor in Chattanooga, Tennessee, about 300 people, built an agent in the spring of 2025 that read restock requests from branch managers and placed replenishment orders through its ERP’s API. It worked for eleven weeks. Nobody touched it. Then, over one weekend in August, the ERP slowed down, responses started arriving after the agent framework’s timeout, and the framework retried. The first calls had gone through. Every retry placed another order. By Monday the distributor had thirty-seven duplicate purchase orders, roughly $186,000 of copper line sets and condensing units, and one supplier had already shipped two truckloads.

The engineer who built it was talented. Really. He had been hired after three interviews about retrieval, prompt design, and which vector database he preferred. Nobody asked him what happens when a tool call’s result never comes back, because nobody on that panel had built software that spends money before.

Payments engineers solved this long ago. Stripe’s documentation on idempotent requests describes it plainly. Send a unique key with the request, and if the connection fails you can repeat it without creating a second object or performing the update twice. A good agent engineer has carried that idea into every tool that changes something. The key is built from the task, not from the attempt. Duplicates are detected on the receiving side, not by hoping the model remembers what it did. When we helped the Chattanooga distributor hire a second engineer that fall, this was the first technical question, and the person they hired had already built exactly that for a freight-booking agent at her previous job.

How does your agent know it is finished?

Short question. Long answers. From the good ones, anyway.

Anthropic’s engineering guide, Building Effective Agents, published in December 2024, recommends stopping conditions such as a maximum number of iterations, because an agent working an open-ended problem cannot know in advance how many steps it will take. Without a limit, it keeps going. It keeps spending, too.

Listen for layers. A cap on steps. A cap on spend per task. A timeout on the whole run, separate from the timeout on any single call. Some notion of “no progress,” such as the same tool called with the same arguments three times in a row. And a defined place the task goes when any of those trip, a queue a person actually reads, rather than a log file nobody opens. One candidate we placed last year described an early agent of hers that retried an empty search through the night and ran up a bill of a little over two thousand dollars before breakfast. She was not embarrassed telling it. She had built the spend cap the next morning, and she said so in about ten seconds.

The server restarts at step fourteen of twenty. What happens to the task?

Real agents run long. Some wait days for a human approval, and in those days the machine restarts, a deploy goes out on Thursday afternoon, the container gets moved to another node, and whatever the agent was holding in memory is simply gone unless somebody planned for it.

Weak answers start the task over from step one, which is fine for a summary and dangerous for anything that already sent an email or booked a shipment. Strong answers describe saved state after each step, a way to resume from the last completed one, and the reason resuming is safe, which loops right back to the purchase order question. A candidate who connects the two without being prompted has been paged for this before.

Which MCP servers would you refuse to install, and why?

The Model Context Protocol has become the default way to hand an agent a set of tools. In December 2025, Anthropic donated it to the newly formed Agentic AI Foundation under the Linux Foundation, and the announcement counted more than 10,000 published MCP servers. That is a lot of other people’s code with a direct line to your agent.

A Boise, Idaho, software company, about sixty people, asked this question of every finalist for its first agent hire this spring. The candidate who got the offer answered with a short list of rules rather than a list of products. Nothing that combines a private data source with a way to post to the outside. Nothing whose tool descriptions change between versions without review, since those descriptions go straight into the model’s context. Pinned versions. Its own credentials for each server, never a shared token. Then she asked which servers they were running today. Nobody on the panel knew. That was the right question to ask them.

Tell me about a task you took away from an agent and gave back to ordinary code.

The same Anthropic guide separates workflows, which run a model and its tools along code paths somebody wrote in advance, from agents, which decide their own next step. Its advice amounts to building the plainest thing that works and adding autonomy only once the plain version runs out.

Experienced agent engineers have all done this at least once, usually after watching an agent make the same decision two different ways on two different days. METR’s research on how long a task an agent can complete has found that length doubling roughly every seven months, which is remarkable. The measurement, though, is the task length an agent finishes with 50 percent reliability. Fifty percent. Nobody ships a payroll step at those odds, and the candidates worth hiring know which parts of their system should never have been left to a coin flip.

If a candidate cannot name one thing they moved back into plain code, they either have not shipped much or have not looked closely at what they shipped. Teams still deciding where that line sits can start with our walkthrough on building a first agentic AI workflow.

Thirty Minutes on One Tool Definition

Questions get you most of the way. Not all of it. For the last part, I like to watch a candidate design something.

The best version I have seen came from a specialty insurer in Omaha, Nebraska, that was hiring for its claims operations team in early 2026. The staff engineer running the loop gave every finalist the same short paragraph describing a tool the insurer’s agent would use. It was called issue_refund, it took an account number and an amount, and it returned “OK” or “error.” The instruction was to rewrite it so an agent could use it safely, talking through the choices out loud. No laptop required. A pen worked fine.

Anthropic’s guide makes the point that the interface between an agent and its tools deserves as much design effort as a screen built for people, and this exercise tests exactly that. Here is what the candidate they hired added, in the order she added it.

• A claim ID, required. No claim, no refund.
• An idempotency key built from the claim ID and the refund type, which nobody had asked for, and which she explained in one breath as “because the network will drop this call someday and I do not want to be the reason somebody gets paid twice.”
• Anything above $500? It would not execute. It would open an approval request for an adjuster instead, with the agent’s reasoning attached.
• Dry run.
• Then she rewrote the errors. “Account closed” and “amount exceeds policy limit” came back as separate plain sentences the model could act on, instead of the single word the original returned.

Two other finalists spent the half hour renaming parameters. Tidy work. One more pasted the whole refund policy into the tool’s description and stopped there, which is roughly what you would expect from someone whose last two years were spent writing prompts and who had never once been on the receiving end of a duplicate payment. Good interview otherwise. The claims team hired the candidate who had thought about what the tool should refuse to do.

Keep it that short and that fake. A made-up tool, thirty minutes, inside the interview. Hand a candidate a real integration off your own backlog and you are asking for free consulting, and the strongest candidates will decline politely and take another offer.

Reading Seniority From the Answer

The questions do not change by level. The answers do, depending on how much of the system the candidate has personally owned, and what happened to them when it failed. Use the last column when two candidates look alike on paper and you need to know which one has actually been at that level.

Pay is a separate conversation, and the ranges are wide. Salary aggregators put the median near $150,000 and the 75th percentile north of $270,000, and our AI agent engineer hiring guide lays out why two of those sites can disagree by tens of thousands of dollars for what is essentially the same work. KORE1’s own bands by level are in the AI agent engineer salary guide. If you need a figure for one city, run it through our salary benchmark tool first. It is free.

Where Hiring Panels Split on Agent Candidates

Does it matter whether they built on LangGraph, CrewAI, or the OpenAI Agents SDK?

Less than most panels think, because the hard problems in agent work (permissions, retries, stopping, and state) look the same in every framework and are mostly solved outside it.

A candidate who has shipped on one framework will pick up another in a couple of weeks. What does not transfer as easily is judgment about what the agent should be allowed to do. Ask why they chose the framework they used. “The team already had it” is an honest answer. So is “we outgrew it and wrote our own loop.”

A strong backend engineer has never shipped an agent. Worth interviewing?

Often, yes, especially one who has built payment, booking, or inventory systems, since idempotency, retries, and approval flows are already second nature to them.

Some of the best agent hires we have made came from exactly that background, people who spent five or six years making sure a hotel room or a wire transfer happened exactly once no matter how many times a phone app retried, and who then found that an agent is mostly a new kind of caller. Run the same loop. Skip nothing. If they answer the purchase order question better than the candidates with “agent” in their title, believe what you heard.

Should candidates be allowed to use a coding agent during the interview?

For a coding exercise, allow it and watch how they supervise it, because supervising an agent is a large part of the job you are hiring for.

Pay attention to whether they read what the assistant wrote before running it. The tool-definition exercise above does not need one at all. It is a design conversation, and a pen is enough.

How long should the loop be?

Three rounds is enough for most seats, a thirty-minute screen, a ninety-minute technical session built around the tool-definition exercise, and a last meeting where someone from security sits in.

The security person is not there to quiz anyone. They are there because they will be living with the agent, and their read on how the candidate talks about risk is worth more than a fourth technical round. Agent engineers who are good tend to have competing offers within two weeks. Every extra round costs you some of them.

Is an “agentic AI engineer” a different job from an “AI agent engineer”?

In practice, no, since the two titles describe the same work, and which one a company posts usually depends on the year it wrote the job description.

Search for both when you source, because candidates use both on their profiles. Our longer agentic AI engineer hiring guide covers the intake questions and sourcing side of that search.

We need one agent built. Contract engineer or full-time hire?

Contract works well for a first build with a defined scope, as long as someone permanent on your team owns the agent after the contractor leaves.

Agents are not finished when they launch. Ever. Tools change, models get replaced, and somebody has to keep the permission list honest. We place agent engineers through contract staffing for builds like that and through direct hire for the long-term owner, and plenty of clients start with the first and convert later through contract-to-hire.

Ask What It Could Delete

That distributor in Chattanooga now asks the purchase order question in every engineering interview it runs, including for jobs with no agent anywhere near them. The engineer they hired that fall owns that part of the loop. Most candidates, she says, go straight to longer timeouts. A few ask whether the ERP will accept an idempotency key. She has stopped being surprised by which group gets hired.

None of the seven questions has a secret answer, and a well-read candidate can get partway through any of them. What they cannot fake is the incident. Where it happened, what the agent did, what they changed on Monday. Ask for that part. It is short when it is real.

If you would rather not run the search alone, bring it to our AI recruiting desk. The same AI recruiters also fill generative AI engineer seats, which is often the role sitting next to this one. KORE1 has been placing technical people since 2005, across thirty-plus U.S. metros, and our placements hold at 92 percent after the first year. Start with what the agent could delete.