Back to AI Agents / RAG
About this interview
A technical interview on Agentic Design Patterns, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Compare LangGraph, CrewAI, AutoGen, and LlamaIndex Agents on the axes of flexibility, abstractions, and production readiness
Explain how LangGraph represents agent workflows as directed graphs with conditional edges
Describe how CrewAI models role-based multi-agent teams and how tasks are assigned and chained
Identify the tradeoffs between high-level framework abstractions and custom agent loop implementations
Topics covered
frameworks-motivationlanggraph-fundamentalslanggraph-conditional-routinglanggraph-tool-integrationlanggraph-memorylanggraph-multi-agentlanggraph-observabilitycrewai-fundamentalscrewai-task-chainingcrewai-designautogen-fundamentalsautogen-agentchatautogen-corellamaindex-agents
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In broad strokes, what problem does an agent orchestration framework like LangGraph or CrewAI solve that you couldn't handle by just calling an LLM API directly?
“What is CrewAI's core metaphor — what three objects does it ask you to define, and how do they relate to each other?
“LlamaIndex Agents positions itself as a data-centric agent framework. How does that emphasis differ from LangGraph's control-flow emphasis and CrewAI's role-based emphasis?