Top 30 Agentic AI Interview Questions and Answers for 2025

Basic Agentic AI Interview Questions

We’ll start with some basic questions that provide definitions and set the tone for the article. A few of them also include tips on what to prepare in advance.

What are some AI applications you have worked on?

Interviewers will want to hear about your experience in a personal and detailed way. They won’t just be looking for a list of projects—since they likely already have that from your resume—but will be evaluating how clearly you can explain each project and your specific role in it.

Make sure to prepare your response in advance and have a clear understanding of your past work. Practicing with a friend or writing it down can help you organize your thoughts.

Which libraries, frameworks, and tools do you have experience with? What other libraries have you heard about?

Similar to the previous question, interviewers will want to hear more than what’s listed on your resume. Be prepared to break down each project you’ve worked on and explain all the technologies used.

Keep in mind that you may be asked many follow-up questions at this point. It’s important for an employer to understand your precise skillset. Make sure to review or familiarize yourself with libraries like LlamaIndex or LangChain, as these are the most common high-level development libraries used. Additionally, get comfortable with model providers such as Huggingface or Ollama.

What is agentic AI, and how does it differ from traditional AI?

Agentic AI refers to artificial intelligence systems that can act autonomously, set their own goals, and adapt to changing environments. In contrast, traditional AI typically operates on predefined rules, taking inputs and producing outputs.

For examples, you can discuss your own projects or mention other agentic AI applications you’ve used or heard about. For a more in-depth explanation of each, I recommend reading the following article on agentic AI.

What excites you about working with agentic AI?

This is a common question designed to understand your motivations and interests. Usually very open ended, the question allows you to go into any direction and genuinely talk to the interviewer.

Have a nice story or an explanation ready for this, make sure to be enthusiastic, specific and try to talk about something connected to the role. If you can’t pin something particular down, try talking about a product you use and why it is exciting or interesting.

Can you give an example of an agentic AI application and talk about its components?

For example, let’s talk about a self-driving car. First, consider the objectives the car needs to accomplish: it must autonomously drive and navigate roads, construct optimal routes, avoid obstacles, and, most importantly, keep the passengers safe.

Once the goals are set, we can look at how the application might be structured. A main model could be responsible for driving the car, taking continuous or on-demand input from smaller models that handle tasks like route optimization or environmental information retrieval.

During the interview, you can go deeper into each of these components. Feel free to come up with your own examples as well.

Which LLMs have you worked with so far?

Be ready to discuss particular models that you have worked with in detail. Employers will want to know how well you understand the model internally. For example, be ready to discuss open-source models like Llama or proprietary GPT models.

This is also a good opportunity to mention new models and show the interviewer that you are keeping up to date. You can for example talk about Deepseek R1 and other reasoning models.

What’s your experience with using LLMs through the API?

This question is about using LLMs through the API instead of a chat window. Be ready to talk about your projects if they use APIs. Make sure to revise using APIs, generating and storing secret keys, monitoring costs, and different model providers. This might also be a good place to talk about your engineering experience.

If you don’t have enough experience with using LLMs through the API, consider these resources:

• GPT-4.5 API Tutorial: Getting Started With OpenAI's API
• DeepSeek API: A Guide With Examples and Cost Calculations
• Mistral OCR: A Guide With Practical Examples

Have you used reasoning models?

With reasoning models like OpenAI o3 and DeepSeek-R1 emerging, employers will want to know about your experience and familiarity with them. It goes beyond simply selecting a different model in an application or API call, as these models produce thinking tokens and often require a different usage pattern.

You could make a good impression if you know how to fine-tune and run locally an open-source model since this is something that the company you’re interviewing for might need. For practice, consider fine-tuning DeepSeek-R1 and running it locally:

• Fine-Tuning DeepSeek R1 (Reasoning Model)
• How to Set Up and Run DeepSeek-R1 Locally With Ollama

Do you use LLMs in your daily workflow? If so, what for?

If you use LLMs in your workflow, this might be your chance to show off your expertise. You can talk about tools you have used, what you liked or disliked about them, and even stuff you are looking forward to. Consider mentioning popular tools like Cursor, NotebookLM, Lovable, Replit, Claude Artifacts, Manus AI, etc.

What are some sources you use to stay up to date with agentic AI?

Some of the employers will want to know how up-to-date you are or can be with AI. Sources you might include in your answer are AI conferences, forums, newsletters, and so on.

How comfortable are you with reading and understanding papers and documentation?

Reading literature, papers, and documentation is a part of almost any AI job. You might also be asked about your general approach to learning or retrieving information. It’s a good idea not to come across as overly reliant on chatbots in your response.

Be prepared to talk about a recent paper you’ve read—for example, you can talk about Google’s Titans Architecture.

Intermediate Agentic AI Interview Questions

Now, with the basic questions out of the way, we can dig a little bit deeper and discuss some intermediate questions that might be asked or serve as a good reference.

What are your views about the ethics of this role and agentic AI in general?

I think this is a pretty rare question, but still a good thing to think about, maybe even generally and not particularly for an interview. You can think about ideas tangent to the role you are applying for or more general ideas like AI applications making decisions that affect human lives. The question definitely does not have a correct answer and generally just serves to check how much you care or have thought about the field.

What security risks should be considered when deploying autonomous AI agents?

There are several security concerns to keep in mind when deploying autonomous AI agents. One risk is that the model may have access to sensitive internal tools or databases. If the model isn’t properly sandboxed or permissioned, a malicious user might use prompt injection or adversarial inputs to extract private data or trigger unintended actions.

Another risk involves manipulation of the model’s behavior through carefully crafted prompts or external inputs. An attacker could induce the model to ignore safety constraints, escalate privileges, or behave in ways that deviate from its intended function.

There’s also the possibility of denial-of-service-style attacks—where the model is overwhelmed with requests or tricked into halting its own operations. If an agent controls critical infrastructure or automated workflows, this could lead to larger disruptions.

To mitigate these risks, it’s important to apply principles from traditional software security: least privilege, rigorous input validation, monitoring, rate limiting, and ongoing red-teaming of the agent’s behavior.

What human jobs do you think will soon get replaced by agentic AI applications and why?

Interviewers might ask this question to understand your grasp of agentic AI capabilities as they stand today. They won’t just be looking for a list, but for a thoughtful explanation of your reasoning.

For example, I personally don’t think doctors will be replaced anytime soon—especially those whose decisions directly affect human lives—and that ties back to ethics. There’s a lot to explore here, and you can even discuss whether you think it’s a good or bad thing for certain jobs to be replaced by AI.

Can you describe some challenges you have faced when working on an AI application?

Even though this is a “you” question, I put it in the intermediate section because it’s quite common and interviewers tend to give it a lot of weight. You definitely need to have a solid example prepared—don’t try to come up with something on the spot.

If you haven’t faced any major challenges yet, try at least to talk about a theoretical situation and how you would handle it.

Advanced Agentic AI Interview Questions

Lastly, let’s discuss some more advanced and technical questions. I will try to be as general as possible, though generally during the real interview questions might be more specific. For example, instead of asking about indexing in general, you might get asked about different indexing methods that Langchain or Llama-Index support.

What is the difference between the system and the user prompt?

System and user prompts are both inputs given to a language model, but they serve different roles and usually carry different levels of influence.

The system prompt is a hidden instruction that sets the overall behavior or persona of the model. It’s not directly visible to the user during a conversation, but it plays a foundational role. For example, the system prompt might tell the model to act like a helpful assistant, a mathematician, or a travel planner. It defines the tone, style, and constraints for the interaction.

The user prompt, on the other hand, is the input that the user types in directly—like a question or a request. This is what the model responds to in real time.

In many setups, the system prompt carries more weight, helping maintain consistent behavior across sessions, while the user prompt drives the specific content of each reply.

How do you program an agentic AI system to prioritize competing certain goals or tasks?

Agentic AI systems are typically programmed by defining clear objectives, assigning appropriate tools, and structuring the logic that determines how the agent prioritizes tasks when goals compete. This often involves using a combination of prompts, function calls, and orchestration logic—sometimes across multiple models or subsystems.

One approach is to define a hierarchy of goals and assign weights or rules that guide the agent in choosing which task to pursue when conflicts arise. Some systems also use planning components or intermediate reasoning steps (like reflection loops or scratchpads) to evaluate trade-offs before acting.

If you’re new to this, I recommend starting with Anthropic’s article on agent design patterns. It offers concrete examples and common architectures used in real-world systems. Many of the concepts will feel familiar if you have a background in software engineering, especially around modular design, state management, and asynchronous task execution.

How comfortable are you with prompting and prompt engineering? What approaches have you heard about or used?

Prompt engineering is a major component of an agentic AI system, but it’s also a topic that tends to invite stereotypes—so it’s important to avoid vague statements about its importance and instead focus on the technical details of how you apply it.

Here’s what I’d consider a good answer:

I’m quite comfortable with prompting and prompt engineering, and I’ve used several techniques in both project work and day-to-day tasks. For example, I regularly use few-shot prompting to guide models toward a specific format or tone by providing examples. I also use chain-of-thought prompting when I need the model to reason step by step—this is especially useful for tasks like coding, logic puzzles, or planning.

In more structured applications, I’ve experimented with prompt tuning and prompt compression, especially when working with APIs that charge by token count or require tight control over outputs. These techniques involve distilling prompts to their most essential components while preserving intent and performance.

Since the field is evolving quickly, I make a habit of reading recent papers, GitHub repos, and documentation updates—keeping up with techniques like function calling, retrieval-augmented prompting, and modular prompt chaining.

What is a context window? Why is its size limited?

A context window refers to the maximum amount of information—measured in tokens—that a language model can process at once. This includes the current prompt, any previous conversation history, and system-level instructions. Once the context window limit is reached, older tokens may be truncated or ignored.

The reason the context window is limited comes down to computational and architectural constraints. In transformer-based models, attention mechanisms require computing relationships between all tokens in the context, which grows quadratically with the number of tokens. This makes processing very long contexts expensive and slow, especially on current hardware. Earlier models like RNNs didn’t have a strict context limit in the same way, but they struggled to retain long-range dependencies effectively.

What is retrieval-augmented generation (RAG)?

Retrieval-augmented generation (RAG) is a technique that improves language models by allowing them to retrieve relevant information from external sources before generating a response. Instead of relying solely on what the model has learned during training, RAG systems can access up-to-date or domain-specific data at inference time.

A typical RAG setup has two main components: a retriever, which searches a database or document collection for relevant context based on the input query, and a generator, which uses that retrieved information to produce a more accurate and informed response. This approach is especially useful for tasks that require factual accuracy, long-term memory, or domain-specific knowledge.

What other LLM architectures have you heard about outside of the transformer?

While the transformer is the dominant architecture in AI today, there are several other model types worth knowing about. For example, xLSTM builds on the LSTM architecture with enhancements that improve performance on long sequences while maintaining efficiency.

Mamba is another promising architecture—it uses selective state space models to handle long-context processing more efficiently than transformers, especially for tasks that don’t require full attention over every token.

Google’s Titans architecture is also worth looking into. It’s designed to address some of the key limitations of transformers, such as the lack of persistent memory and high computational costs.

These alternative architectures aim to make models more efficient, scalable, and capable of handling longer or more complex inputs without requiring massive hardware resources.

What are tool use and function calling in LLMs?

Tool and function calling allows large language models to interact with external systems, such as APIs, databases, or custom functions. Instead of relying solely on pre-trained knowledge, the model can recognize when a task requires up-to-date or specialized information and respond by calling an appropriate tool.

For example, if you ask a model with access to a weather API, “What’s the weather in London?”, it can decide to call that API in the background and return the real-time data instead of generating a generic or outdated answer. This approach makes models more useful and reliable, especially for tasks involving live data, computations, or actions outside the model’s internal capabilities.

What is chain-of-thought (CoT), and why is it important in agentic AI applications?

Chain-of-thought (CoT) is a prompting technique that helps language models break down complex problems into step-by-step reasoning before producing a final answer. It allows the model to generate intermediate reasoning steps, which improves accuracy and transparency, especially for tasks involving logic, math, or multi-step decision-making.

CoT is widely used in agentic AI systems. For example, when a model is acting as a judge in an evaluation, you might prompt it to explain its answer step-by-step to better understand its decision process. CoT is also a core technique in reasoning-focused models like OpenAI o1, where the model first generates reasoning tokens before using them to produce the final output. This structured thinking process makes agent behavior more interpretable and reliable.

What is tracing? What are spans?

Tracing is the process of recording and visualizing the sequence of events that occur during a single run or call of an application. In the context of LLM applications, a trace captures the full timeline of interactions—such as multiple model calls, tool use, or decision points—within one execution flow.

A span is a single event or operation within that trace. For example, a model call, a function invocation, or a retrieval step would each be recorded as individual spans. Together, spans help you understand the structure and behavior of your application.

Tracing and spans are essential for debugging and optimizing agentic systems. They make it easier to spot failures, latency bottlenecks, or unintended behaviors. Tools like Arize Phoenix and others provide visual interfaces to inspect traces and spans in detail.

What are evals? How do you evaluate the performance and robustness of an agentic AI system?

Evals are essentially the unit tests of agentic AI engineering. They allow developers to assess how well the system performs across different scenarios and edge cases. There are several types of evals commonly used today. One approach is to use a hand-crafted ground-truth dataset to compare the model’s outputs against known correct answers.

Another approach is to use an LLM as a judge to evaluate the quality, accuracy, or reasoning behind the model’s responses. Some evals test overall task success, while others focus on individual components like tool use, planning, or consistency. Running these regularly helps identify regressions, measure improvement, and ensure the system remains reliable as it evolves. For a deeper dive, I recommend checking out this LLM evaluation guide.

Can you talk about the transformer architecture and its significance for agentic AI?

The transformer architecture was introduced in the influential 2017 paper “Attention Is All You Need.” If you haven’t read it yet, it’s worth going through—it laid the foundation for nearly all modern large language models.

Since its release, many variations and improvements have been developed, but most models used in agentic AI systems are still based on some form of the transformer.

One key advantage of the transformer is its attention mechanism, which allows the model to compute the relevance of every token in the input sequence to every other token, as long as everything fits within the context window. This enables strong performance on tasks that require understanding long-range dependencies or reasoning across multiple inputs.

For agentic AI specifically, the transformer’s flexibility and parallelism make it well-suited for handling complex tasks like tool use, planning, and multi-turn dialogue—core behaviors in most agentic systems today.

What is LLM observability, and why is it important?

LLM observability refers to the ability to monitor, analyze, and understand the behavior of large language model systems in real time. It’s an umbrella term that includes tools like traces, spans, and evals, which help developers gain visibility into how the system operates internally.

Since LLMs are often seen as “black boxes,” observability is essential for debugging, improving performance, and ensuring reliability. It allows you to trace how models interact with each other and with external tools, identify failure points, and catch unexpected behaviors early. In agentic AI systems, where multiple steps and decisions are chained together, observability is especially critical for maintaining trust and control.

Can you explain model fine-tuning and model distillation?

Model fine-tuning is the process of taking a pre-trained model and training it further on a new dataset, usually to specialize it for a specific domain or task. This allows the model to adapt its behavior and responses based on more focused or updated knowledge.

Model distillation is a related technique where a smaller or less capable model is trained on the outputs of a larger, more powerful model. The goal is to transfer knowledge and behavior from the larger model to the smaller one, often resulting in faster and more efficient models with comparable performance. For example, since Deepseek R1’s release, many smaller models have been distilled on its responses and have achieved impressive quality relative to their size.

What is the next token prediction task, and why is it important? What are assistant models?

Next token prediction, also known as autoregressive language modeling, is the core training task behind most large language models. The model is trained to predict the next token in a sequence given all the previous tokens. This simple objective enables the model to learn grammar, facts, reasoning patterns, and even some planning capabilities. The result of this initial training phase is called a base model.

Assistant models are base models that have been further fine-tuned to behave more helpfully, safely, or conversationally. This fine-tuning usually involves techniques like supervised instruction tuning and reinforcement learning with human feedback (RLHF), which guide the model to respond more like an assistant rather than just completing text.

What is the human-in-the-loop (HITL) approach?

The human-in-the-loop (HITL) approach refers to involving humans in the training, evaluation, or real-time use of an LLM or agentic AI system. Human input can happen at various stages—during model training (e.g., labeling data, ranking responses), during fine-tuning (such as in RLHF), or even during execution, where a human might guide or approve an agent’s actions.

For example, if a chatbot asks you to choose the better of two responses, you’re actively participating in a HITL process. This approach helps improve model quality, safety, and alignment by incorporating human judgment where automation alone may fall short.

Conclusion

In this article, we covered a range of interview questions that might come up in an agentic AI interview, along with strategies for thinking through, researching, and answering them effectively. For further study, I recommend exploring the references mentioned throughout the article and checking out DataCamp’s AI courses on the subject for more structured learning.