Technical practice
How would you design a multi-agent orchestration architecture using LangGraph or AutoGen to handle complex SDLC tasks like automated code generation and test creation?
What to coverThe candidate should discuss state management, routing strategies, cyclic recovery, and tool delegation. They should address how intermediate agent states are persisted and validated to avoid infinite loops or drift during multi-step workflows. As a preparation suggestion, highlight the trade-offs between graph-based deterministic flows and dynamic multi-agent conversational patterns.
What architectural patterns and data validation strategies would you use with FastAPI and Pydantic when serving LLM agent services?
What to coverThe response should cover asynchronous endpoint handling, streaming responses (such as SSE) for real-time agent output, and strict input/output schema validation. The candidate should explain how Pydantic models prevent malformed tool inputs and enforce type safety when parsing structured LLM responses. Suggest reviewing latency considerations and error handling for upstream LLM timeouts.
How do you design and optimize an enterprise RAG pipeline using vector databases to provide contextual intelligence to software engineering agents?
What to coverThe candidate should address indexing strategies for source code and technical documentation, embedding model selection, chunking techniques, and metadata filtering. They should discuss hybrid search mechanics and re-ranking to balance retrieval recall against context window limitations. Suggest articulating strategies for refreshing vector embeddings as codebases update incrementally.
How do you securely integrate autonomous AI agents with developer toolchains like GitHub, Bitbucket, Jira, and Terraform?
What to coverThe candidate should explain webhook ingestion, authentication mechanisms (e.g., fine-grained tokens, IAM roles), and sandboxed execution environments. Key trade-offs include granting write or merge permissions versus requiring explicit human-in-the-loop approvals for pull requests and infrastructure deployments. Suggest discussing least-privilege access and blast-radius containment.
How would you deploy and scale an agentic AI service on AWS using EKS, Lambda, S3, and Terraform?
What to coverThe candidate should detail containerizing the agent application with Docker, managing deployment manifests via Terraform, and determining when to use serverless Lambda versus long-running EKS pods. They should address stateless API scaling, storing agent session history or artifacts in S3, and managing AWS IAM permissions securely. Suggest contrasting the cost, cold-start latency, and execution timeouts across compute options.
What validation standards and automated testing frameworks would you institute to evaluate the correctness and security of LLM-generated code?
What to coverThe candidate should detail multi-stage verification pipelines including static analysis (AST parsers, linters), automated unit and integration test generation, and sandboxed test execution. They should address hallucinated dependencies, syntax validity, and automated benchmarking against human-authored baselines. Suggest discussing how to define measurable quality and pass/fail thresholds before code reaches review.
How do you enforce responsible AI practices, data sensitivity boundaries, and prompt injection defenses in enterprise developer agents?
What to coverThe candidate should explain sanitization of prompts and responses to prevent leaking sensitive IP or credentials into external model APIs. They should discuss mitigation strategies for prompt injection, context manipulation, and guardrails enforcing compliant behavior. Suggest outlining role-based access controls and policy enforcement layers embedded within agent middleware.
How do you instrument observability, distributed tracing, and monitoring for multi-turn LLM agent workflows across AWS and enterprise platforms?
What to coverThe candidate should discuss tracing individual LLM calls, tool executions, latency, token consumption, and failure modes across complex agent graphs. They should cover integration with monitoring platforms, custom metrics collection, and alerting for degradation or abnormal token usage. Suggest explaining how distributed trace contexts propagate across asynchronous agent steps.
How do frameworks like Model Context Protocol (MCP) and A2A SDK facilitate standardized tool and agent communication?
What to coverThe candidate should compare standardized agent interfaces and protocols against custom API wrappers for tool exposure and inter-agent communication. They should discuss contract definitions, capability discovery, error handling, and cross-framework interoperability. Suggest demonstrating an understanding of how standard protocols simplify adding new toolchains to existing agents.
Behavioural practice
Describe a situation where you had to drive adoption of AI-assisted engineering practices across resistant or skeptical developer teams.
What to coverThe candidate should use the STAR method to describe how they established trust, demonstrated concrete productivity and code quality gains, and addressed developer concerns regarding reliability. They should highlight leadership tactics, measurable outcomes, and how feedback was gathered to adjust practices. Suggest focusing on balance between engineering freedom and standard compliance.
Tell me about a time you established governance, security, or validation standards for a newly adopted technology stack.
What to coverThe candidate should detail a real-world scenario where they defined validation controls, secure coding policies, or automated review gates for emerging tools. They should explain the actions taken to coach other senior engineers and ensure adherence without severely slowing release cadence. Suggest emphasizing measurable results such as reduced defect rates or compliant audit findings.
Can you share an experience where an autonomous system or automated workflow produced an unexpected failure or severe hallucination in production?
What to coverThe candidate should structure their answer around diagnosing the root cause, mitigating immediate impacts, and redesigning the system to prevent recurrence. They should cover technical adjustments (e.g., tighter schema validation, human approval gates) and stakeholder communication. Suggest demonstrating accountability and systematic post-incident learning.
Describe how you have mentored and upskilled senior engineers and junior team members in complex, evolving technical domains like AI.
What to coverThe candidate should illustrate structured mentorship, code pairing, or workshop initiatives they led to elevate team capability. The narrative should outline how they tailored technical guidance across varying seniority levels and fostered autonomous problem-solving. Suggest highlighting tangible outcomes, such as engineers successfully delivering complex sub-systems independently.