"How would you design a multi-agent system to handle a complex task?"

Multi-agent design is currently one of the most common technical questions in AI engineering interviews. Candidates who name the orchestrator's role and the failure modes land stronger offers than those who only describe the happy path.

Miss one and they probe.

1. Decompose — justify why this task needs multiple agents at all
2. Topology — name the agents, their roles, the pipeline shape
3. Orchestrator — plain code for deterministic routing, not an LLM, and why
4. Handoff validation — what you check, what happens on failure
5. Failure modes — agent loops, prompt injection, silent corruption

"First I'd check whether this task actually needs multiple agents — if it exceeds one context window, if different steps need genuinely different expertise, or if steps can run in parallel. Otherwise I'd start with a single agent. If justified, I'd name the agents and map the pipeline shape — linear, parallel fanout, or tree. I'd use plain code as the orchestrator for fixed pipelines, since routing should be deterministic — an LLM orchestrator only makes sense when the routing decision itself requires reasoning. At every handoff I check non-empty output, correct format, and expected length; on failure I retry once, then raise an error rather than passing bad data downstream — silent corruption, where garbage propagates and the system still reports success, is the hardest failure mode to catch in production. I'd also design against agent loops with a max-turn limit, and prompt injection by treating every inter-agent message as untrusted."