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The author details an experimental multi-agent orchestration framework using a directed acyclic graph (DAG), concentrating intelligence in planner and replanner components while keeping worker agents mechanical. They are seeking community feedback, benchmarks, and existing research to validate its practicality against conventional message-passing approaches.
The author introduces DRIFT, a local AI system built with Python and Ollama that features persistent memory, simulated somatic feedback, and Jungian psychological modeling to create a more grounded, sovereign AI interaction.