Routing with small models is 200x faster and 400x cheaper than LLMs, reshaping AI software architecture
Pavan_Belagatti · x · 2026-09-29
Pavan Belagatti maps the shift in AI-era software architecture: traditional software relied on rigid hardcoded conditionals; early agentic systems over-corrected by routing every task through a single expensive frontier LLM. The emerging pattern pairs deterministic code that maintains workflow structure with fast, specialized decision models (like Jev) for narrow semantic branching. Treating judgment as a low-cost primitive makes routing up to 200x faster and 400x cheaper than LLMs, escalating to full reasoning models only on exception. He also highlights LangGraph as the orchestration layer for state, durable execution, and human-in-the-loop checkpoints, combined with agentic SDLC platforms.
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