Multi-Agent Systems Interview Guide: Orchestration, Consensus, and Failure Recovery

Multi-agent system interviews test orchestration discipline, not agent count. Interviewers ask why you need multiple agents instead of a single well-tooled agent, how supervisors delegate without bottlenecks, and how shared state stays consistent when parallel sub-agents write conflicting conclusions.

Supervisor and hierarchical patterns dominate enterprise designs. Candidates explain routing logic, escalation to specialist agents, and termination conditions. Without explicit stop criteria, token costs compound and latency becomes unpredictable—common failure modes in real deployments.

Consensus and conflict resolution arise when research agents disagree or coding agents produce incompatible patches. Discuss voting, critic agents, human arbitration, and deterministic merge strategies. Interviewers prefer concrete protocols over hand-waving about emergent collaboration.

Failure recovery must be designed in. When one sub-agent times out or returns malformed tool output, does the supervisor retry, substitute, or degrade? Describe circuit breakers, per-agent budgets, and tracing that shows which agent contributed to a bad final answer.

Cost control is a first-class requirement. Multi-agent fan-out can multiply LLM calls by an order of magnitude. Articulate batching, caching intermediate results, and when to collapse multi-agent workflows into single-pass pipelines after prototyping proves marginal value.