What is an Agentic AI Engineer at Google?
As an Agentic AI Engineer at Google, you will stand at the forefront of the next major paradigm shift in technology: transitioning from static, retrieval-based artificial intelligence to active, autonomous, and goal-oriented agents. This role is not merely about prompt engineering or wrapper development. It is about building production-grade, highly reliable software systems that leverage Google’s cutting-edge foundation models—such as Gemini and products from Google DeepMind—to execute complex, multi-step tasks in real-world environments.
You will work on highly strategic initiatives across various product areas. For instance, in the Applied AI and Food AI teams, you will build and scale intelligent conversational agents deployed in high-throughput, low-latency environments like drive-thrus and call centers. In other teams, such as BigQuery Agentic AI or Google Distributed Cloud (GDC), you will design the infrastructure, tool-calling frameworks, and distributed systems required to run agentic workloads securely at massive scale. Your work will directly impact how billions of users and enterprise clients interact with automated systems, turning natural language into precise, deterministic actions.
This role requires a unique intersection of skills. You must possess the rigorous computer science fundamentals expected of any Google software engineer—including strong data structures, algorithms, and system design—combined with deep domain expertise in mobile application development (Android, iOS, or Flutter), distributed systems, and modern LLM orchestration. It is a highly demanding but immensely rewarding position where you will define the architecture of autonomous software.
Common Interview Questions
See every interview question for this role
Sign up free to access the full question bank for this company and role.
Sign up freeAlready have an account? Sign inPractice questions from our question bank
Curated questions for Google from real interviews. Click any question to practice and review the answer.
Sign up to see all questions
Create a free account to access every interview question for this role.
Sign up freeAlready have an account? Sign in`
Getting Ready for Your Interviews
Preparing for an Agentic AI Engineer interview at Google requires a balanced study plan that addresses both traditional software engineering excellence and modern AI system design. You cannot rely solely on your AI portfolio; you must also demonstrate the core coding and system design capabilities that define a Google engineer.
Role-Related Knowledge (RRK) – You must show a deep, practical understanding of how LLMs work under the hood, how to build agentic feedback loops (such as ReAct or Plan-and-Solve patterns), and how to integrate these models with external APIs and databases. If you are applying to a mobile-focused team, this also includes demonstrating production-level mastery of Android, iOS, Flutter, and Dart.
General Cognitive Ability (GCA) – Google interviewers use GCA questions to assess how you think, learn, and adapt to novel, ambiguous situations. You will be presented with open-ended problems—such as scaling a system to handle a 10x spike in traffic or resolving a complex technical bottleneck—and evaluated on how logically you break down the problem, gather requirements, and formulate structured solutions.
Googleyness & Leadership (G&L) – This is Google’s unique behavioral evaluation. Interviewers want to see how you align with Google’s core values: doing the right thing, striving for diversity and inclusion, acting with humility, and showing a bias for action. You should be prepared to discuss how you lead project initiatives, handle cross-functional conflict, navigate ambiguity, and advocate for safe, responsible AI practices.
Interview Process Overview
The interview process for an Agentic AI Engineer at Google is rigorous, structured, and designed to evaluate both your general software engineering capabilities and your domain-specific AI expertise. The entire loop typically takes between 4 to 8 weeks from the initial recruiter contact to the final offer decision.
The journey begins with a recruiter screen, followed by a technical screening phase (usually one or two coding interviews conducted via Google Meet). Once you clear the screen, you will move to the onsite loop. The onsite loop is the most intensive part of the process, consisting of 4 to 5 interviews covering coding, system design (with a heavy emphasis on agentic architectures and LLM integration), and behavioral attributes.
Google's hiring philosophy values strong computer science fundamentals, structured problem-solving, and collaborative communication. They are looking for "smart creatives" who do not just memorize solutions but can think on their feet when faced with highly ambiguous, scale-related challenges.
`
`
The visual timeline above outlines the typical progression of the candidate journey. Use this sequence to pace your preparation, focusing first on passing the rigorous coding screens before shifting your attention to complex system architecture and behavioral scenarios for the onsite loop.
Deep Dive into Evaluation Areas
To stand out in the interview loop, you must understand exactly what Google interviewers look for in each specialized domain. Below is a detailed breakdown of the primary evaluation areas for the Agentic AI Engineer role.
Agentic Architecture & Tool Use
This is the core of the role. You must prove that you can build autonomous systems that can safely, reliably, and efficiently interact with the physical and digital world.
Be ready to go over:
- ReAct & Plan-and-Solve Frameworks – How to structure LLM prompts and execution loops so the model can reason, plan, and take actions sequentially.
- Function Calling & Tool Binding – Techniques for mapping natural language user intent to structured API schemas, and parsing model outputs back into programmatic actions.
- State and Memory Management – Designing robust architectures to maintain conversational state, session context, and long-term memory across distributed systems.
- Advanced concepts (less common) – Multi-agent orchestration, self-reflection/error-correction loops, and using reinforcement learning from AI feedback (RLAIF) to optimize agent pathways.
Example questions or scenarios:
- "Design a system where an AI agent must write, test, and execute its own database queries to answer a complex customer billing question, ensuring it never corrupts production data."
- "How would you design a fallback mechanism for an autonomous agent when an external third-party API it relies on suddenly rate-limits or fails?"
Mobile & Edge AI Integration
For teams working on client-facing applications like the Food AI Agent, your ability to deliver a seamless, low-latency mobile experience is critical.
Be ready to go over:
- Cross-Platform Development – Developing performant, responsive UIs using Flutter and Dart that handle complex asynchronous data streams.
- On-Device vs. Cloud Execution – Evaluating the latency, privacy, cost, and power-consumption trade-offs of running local models (e.g., Gemini Nano) versus cloud APIs.
- Streaming Audio & Speech Pipelines – Designing efficient client-side buffering and streaming protocols to send user voice inputs to Speech-to-Text models with minimal latency.
- Advanced concepts (less common) – Custom hardware acceleration (TPUs/GPUs on device), model quantization techniques, and writing custom platform channels in native Android (Kotlin) or iOS (Swift).
Example questions or scenarios:
- "A user is ordering food via voice in a noisy car. Design the mobile-side audio capture and streaming architecture to ensure the backend agent receives the cleanest possible audio signal."
- "Explain how you would minimize battery drain on an iOS device while maintaining a continuous, real-time voice connection to an agentic backend."
System Design at Scale
Agentic workflows are computationally expensive and latency-sensitive. You must demonstrate that you can build backend systems capable of supporting millions of concurrent agent sessions.
Be ready to go over:
- Latency Optimization – Strategies for reducing Time-to-First-Token (TTFT), such as streaming responses, speculative decoding, and semantic caching of LLM prompt/response pairs.
- Distributed State & Concurrency – Managing state consistency when an agent's reasoning loop spans multiple microservices, databases, and external APIs.
- Sovereign & Distributed Cloud Infrastructure – Designing agentic workloads that can run securely within private clouds or air-gapped environments like Google Distributed Cloud (GDC).
- Advanced concepts (less common) – GPU cluster provisioning, dynamic batching of LLM inference requests, and building custom vector database indexing strategies for real-time RAG (Retrieval-Augmented Generation).
Example questions or scenarios:
- "Design a globally distributed rate-limiting and queuing system for an enterprise agent platform that utilizes a shared pool of Gemini API tokens."
- "How would you architect a logging and observability pipeline to trace the multi-step reasoning path of a failed agent execution across a distributed system?"
`
