Back to AI Agents / RAG
About this interview
A technical interview on Multi-Agent Systems, pitched at the hard 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
Identify scenarios where a single agent should be split into a team: large workloads, diverse expertise, fault tolerance
Compare centralized (orchestrator), decentralized (peer-to-peer), and hybrid coordination architectures
Explain the role of agent communication protocols, shared state, and coordination mechanisms in multi-agent systems
Describe observability requirements — logging, tracing, dashboards — for debugging multi-agent interactions
Topics covered
FoundationsWhen to use multi-agentSpecialization and expertiseReliability and fault toleranceCoordination architecturesCoordination patternsShared state and communicationAgent communication protocolsSystem designFrameworks — CrewAIFrameworks — AutoGenObservability and debuggingSystem design tradeoffsCoordination mechanisms
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In plain terms, what is a multi-agent system, and how does it differ from a single agent that has access to many tools?
“What is the difference between AutoGen AgentChat and AutoGen Core? When would you reach for Core instead of the higher-level AgentChat abstraction?
“In a multi-agent system where agents independently call the same external API, how do you handle rate limits, retries, and deduplication without a central bottleneck?