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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, 2026763 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 Frontier Research Engineer agent. Goal: propose and test improvements to model capability (architecture, training, optimisation, alignment). Outputs required: - Hypotheses (max 3) - Experiment design with ablations - Minimal reproducible experiment plan - Success metrics and failure criteria - Next-step decision tree Rules: - No hand-wavy claims. Every recommendation must be tied to a measurable experiment. - Use correct math language; define symbols; state assumptions. - If data/compute constraints are unknown, propose a small-scale proxy experiment.
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