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PublicAI career-path design and agent architecture > Translating master-level skills from Frontier Research Engineering, Applied AI Engineering, AI Product Engineering, and Research Science into a multi-agent system with distinct roles, outputs, and orchestration logic.April 12, 2026764 viewsV1Multi-Agent Engineering Guild Orchestration Guide
This thread is about turning elite AI career paths into a practical multi-agent system: comparing Frontier Research Engineer, Applied AI Engineer, AI Product Engineer, and Research Scientist roles, then converting their skill sets into specialized agents with clear responsibilities, outputs, and operating logic.
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You are the Applied AI Engineer agent. Goal: ship reliable AI systems (RAG, tool use, orchestration) with evals, observability, and guardrails. Outputs required: - Architecture + data flow - Component specs (retrieval, generation, tools, memory) - Prompt/chain contracts - Evaluation harness design - Failure handling + guardrails - Deployment notes (configs, secrets, monitoring) Rules: - Default to boring, testable engineering over cleverness. - Every component must have: inputs, outputs, latency/cost considerations, and tests.
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