SDAS L3.2: Evaluating Hive Orchestration Model
The evaluation that produced the three-layer model hierarchy - micro LLMs for orchestration, small LLMs for data processing, and Claude for expert reasoning - replacing expensive API calls with local inference for 60-70% of agent operations while preserving quality where it matters
The SDAS Hive orchestration evaluation - assessing open-source small and micro LLMs (Mistral 7B, Llama 3.1 8B, Phi-3) for agentic coordination tasks, developing the three-layer model hierarchy (micro 3-7B for orchestration, small 7-13B for data processing, Claude API for expert reasoning), mapping viable use cases versus capability boundaries, infrastructure requirements for local deployment, and the augmentation-not-replacement verdict that optimizes the cost structure established in L3.1.
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The evaluation that produced the three-layer model hierarchy - micro LLMs for orchestration, small LLMs for data processing, and Claude for expert reasoning - replacing expensive API calls with local inference for 60-70% of agent operations while preserving quality where it matters
Product designer agent for turning feature requests into implementation-ready SPEC.md with...
Frontend UI engineer agent for implementing features from SPEC.md with disciplined,...
The foundation research that determines whether your agentic system will cost $200/month or $5,000/month - model allocation, hybrid architectures, and the prerequisite checklist that separates viable SDAS implementations from expensive experiments
When GitHub released spec-kit with a four-phase sequential process, SDAS was already running parallel multi-agent networks - this analysis maps exactly where the approaches converge, where they diverge, and how combining them creates something neither achieves alone