Deep explanation
System design: a financial research assistant with AutoGen
Principal system design: an AutoGen multi-agent research assistant for finance with AgentChat teams, termination conditions, Docker code execution, durable state, distributed runtime, audit trails, and a clear case for when multi-agent beats a single agent.
Quick answer
I would use AutoGen AgentChat for the workflow: a data agent with read-only data tools, an analyst agent that runs Python in a sandboxed Docker code executor, a writer, and a reviewer that checks every number against tool outputs, arranged in a controlled team with hard termination conditions. Each run is a job with saved state, so it can resume, be audited, and scale across workers on the AutoGen runtime. Multi-agent earns its cost only where separation helps, for example isolating code execution and having an independent review step; simple lookups should skip the team and use one agent.
The interview question
Design a production financial research assistant where AutoGen orchestrates several agents: one gathers filings and market data, one runs quantitative analysis in code, one drafts the research note, and a reviewer checks claims before an analyst sees it. Cover the agent and team design, tool and code execution boundaries, state and scaling, security, observability, evaluation, and rollout. Where does a multi-agent design actually earn its cost here?
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