Question 1
Difficulty: medium
How do you design an agentic AI system that can plan, take actions, and recover from errors without constant human oversight?
Sample answer
I usually start by separating the system into clear layers: planning, tool use, memory, and guardrails. For planning, I prefer a structured loop where the agent decomposes the user goal into smaller steps, checks whether each step is valid, and only then executes. For actions, I keep the tool interface narrow and explicit so the agent can’t do anything ambiguous. Recovery matters just as much as execution, so I design retries, fallback paths, and state checkpoints that let the agent resume safely after a failure. I also add observability from day one: traces, tool call logs, and decision summaries so I can see why the agent behaved a certain way. In practice, I’ve found that the best agentic systems are not the most autonomous ones—they’re the ones with the right level of autonomy plus strong constraints. That balance gives users trust without making the system brittle.
Question 2
Difficulty: medium
Tell me about a time you had to reduce hallucinations or unsafe actions in an AI agent.
Sample answer
In a previous project, an agent was generating actions that looked correct in language but were not always valid for the underlying system. The biggest issue was that it would confidently choose an action based on partial context. I addressed this by adding schema validation, restricting tool outputs to structured formats, and inserting a verification step before execution. I also changed the prompt so the agent had to state assumptions explicitly and ask for clarification when confidence was low. On the monitoring side, I tracked failed tool calls and grouped them by root cause, which helped us identify patterns instead of fixing issues one by one. That led us to improve the retrieval layer because some bad decisions were caused by stale context, not just model error. The result was a much safer system with fewer incorrect actions and a better user experience, because the agent became more deliberate instead of overconfident.
Question 3
Difficulty: easy
How would you decide when an agent should act autonomously versus when it should ask a human for approval?
Sample answer
I treat autonomy as a product and risk decision, not just a technical one. First I classify actions by impact: low-risk actions like summarizing data or drafting a response can often be fully autonomous, while high-risk actions like changing production data, sending external communications, or making financial decisions should require approval. Then I look at confidence signals, such as model certainty, completeness of context, and whether the tool result matches the plan. I also consider reversibility. If an action is easy to undo, I can allow more autonomy than if the action has legal, financial, or customer-facing consequences. In practice, I like a tiered approach: the agent can execute low-risk steps on its own, but it pauses for approval on sensitive ones. That keeps workflows moving while still protecting the business. I’ve found users trust agentic systems more when the approval boundaries are consistent and easy to understand.
Question 4
Difficulty: hard
What evaluation methods would you use to measure the performance of an agentic AI system?
Sample answer
I’d evaluate an agentic system on more than final answer quality, because good wording can hide bad decision-making. I usually measure task completion rate, tool success rate, number of steps to completion, error recovery rate, and how often the agent asks unnecessary clarification questions. I also look at safety metrics, like policy violations or unauthorized tool calls, because those can be more important than accuracy in production. For more complex workflows, I like scenario-based eval sets that represent realistic edge cases, not just happy paths. If the agent uses retrieval or external tools, I test how it behaves under stale data, empty results, and contradictory inputs. I also compare trace quality, because a system that is easy to debug is easier to improve. In mature environments, I’d add human review of sampled runs to catch failure modes that pure metrics miss. That combination gives a much clearer picture than one benchmark score ever could.
Question 5
Difficulty: hard
Describe how you would build memory for an AI agent so it is useful but does not become noisy or insecure.
Sample answer
I’d split memory into short-term working memory, task memory, and long-term user or organizational memory. Short-term memory should stay close to the current interaction and be trimmed aggressively, because too much context makes the agent less reliable. Task memory should capture only the key decisions, constraints, and completed steps, ideally in a structured form rather than raw conversation text. Long-term memory is where I’m most careful. I store only information that is clearly useful, permissioned, and durable, and I make sure sensitive content is either excluded or protected with strong access controls. I also prefer memory that is retrievable on demand instead of always injected into prompts, since that reduces noise and privacy risk. Another important piece is forgetting. If information is outdated or no longer relevant, it should decay or be archived. Good memory is selective, not exhaustive. The goal is to make the agent smarter over time without turning it into a cluttered notebook.
Question 6
Difficulty: medium
How do you handle prompt engineering and system design together in an agentic workflow?
Sample answer
I see prompt engineering as one part of the control system, not the whole solution. A strong prompt can improve reasoning and behavior, but if the surrounding architecture is weak, the agent still fails in predictable ways. So I design the workflow first: what the agent can see, what tools it can use, when it should pause, and what it should log. Then I shape the prompt around that structure. For example, I’ll give the agent clear role instructions, a step-by-step decision format, and examples of when to escalate or stop. But I also enforce constraints in code, because prompts alone are not reliable enough for safety or correctness. I like to keep prompts short, stable, and aligned with the actual system behavior so they don’t drift from implementation. The best results come when prompt design, state management, and tool governance reinforce each other instead of trying to compensate for each other.
Question 7
Difficulty: medium
Tell me about a time you had to debug an agent that kept failing in production. What was your approach?
Sample answer
When an agent starts failing in production, I try not to guess. My first step is always to reconstruct the run: input, context, retrieved data, tool calls, model output, and any downstream errors. In one case, the agent seemed random, but the trace showed a more specific issue: it was receiving partial search results and then making decisions as if the retrieval step had succeeded fully. That meant the real bug was not in the model but in the orchestration logic. I added better state checks, clearer error handling, and a fallback path when retrieval returned incomplete context. I also created a small set of replay tests from real failures so we could reproduce issues before deploying fixes. That changed debugging from reactive to systematic. My experience is that agent failures are often cross-layer failures, so you need visibility across prompts, tools, and application logic to solve them quickly and permanently.
Question 8
Difficulty: hard
How would you integrate external tools or APIs safely into an AI agent?
Sample answer
I would treat every tool like an attack surface and design for least privilege. The agent should only have access to the specific actions it truly needs, and each tool should accept structured inputs with schema validation. I also prefer a permission layer between the model and the actual API so I can apply policy checks before execution. For sensitive tools, I’d add confirmation steps, rate limits, and idempotency safeguards to avoid duplicate or unintended actions. Another important part is sanitizing tool outputs, because external systems can return noisy or malformed data that the agent should not trust blindly. I also keep a full audit trail of which tool was called, why it was called, and what the result was. That helps with debugging and compliance. In general, I want the agent to be capable, but never unconstrained. Safe integration is what turns a clever demo into a dependable product.
Question 9
Difficulty: easy
Why do you want to work on agentic AI specifically, rather than general machine learning or LLM applications?
Sample answer
I’m interested in agentic AI because it sits at the point where models start creating real operational value. A lot of AI work ends at prediction or content generation, but agentic systems can actually carry a task forward, coordinate steps, use tools, and adapt when something changes. That makes the engineering more challenging and more meaningful. I also like that this space requires a blend of skills: reasoning about system design, reliability, product constraints, and human trust, not just model performance. For me, that combination is exciting because it rewards disciplined engineering, not just experimentation. I’m especially motivated by the fact that small design choices can dramatically affect whether an agent is helpful or frustrating. Building systems that are useful, safe, and observable is the kind of work I enjoy most. It feels like creating infrastructure for the next generation of software, where the interface is no longer just code or menus, but intelligent action.
Question 10
Difficulty: medium
How would you approach a situation where a product manager wants a highly autonomous agent, but you believe the current design is too risky?
Sample answer
I’d start by aligning on the actual business goal instead of debating autonomy in the abstract. Often the PM wants speed, fewer manual steps, or a better user experience—not autonomy for its own sake. Once the goal is clear, I’d explain the specific risks in terms the product team can use: potential customer impact, reversibility, compliance concerns, or operational cost. Then I’d propose a phased rollout. For example, we could launch with assisted mode, where the agent drafts actions and explains its reasoning, then expand to partial autonomy for low-risk tasks, and only later allow full execution in narrow cases. I find this approach works well because it keeps momentum while reducing risk. I also like to define success metrics together, so the team can see whether tighter controls are actually hurting adoption or simply improving trust. Good product decisions in agentic AI usually come from explicit tradeoffs, not from choosing one extreme side.
