agentsop-cost-tiered-models

What problem does it solve?

It helps you design multi-call LLM pipelines so only the few high-cognitive-load steps use a strong (expensive) model, while the many mechanical steps use a cheap (fast) model—preserving quality with far lower total cost.

Core Features & Use Cases

• Cost-aware tiering by cognitive demand: assigns each LM call to a Tier-S (decide) or Tier-E (execute) role based on whether it needs planning/routing/judgment versus formatting/rewrite/extraction/application.
• Escalation valve for long-tail failures: detects Tier-E degradation (e.g., formatting/schema/test failures) and upgrades only the failing step back to Tier-S.
• Cross-framework operational SOP: provides the same “strong reasoner + cheap executor” shape across Aider (architect+editor), DSPy (optimizer-LM vs task-LM), LangGraph (supervisor+worker), and vLLM (speculative draft+target), plus anti-patterns and boundaries.
• Use case examples: coding/agent pipelines, automated refactoring/edit workflows, document transformation chains, and multi-agent orchestrations where most calls are mechanical post-processing.

Quick Start

Ask the system to redesign your multi-step LLM workflow by listing all LM calls, tagging each as “thinking” or “doing,” mapping them to Tier-S vs Tier-E, and adding a step-level escalation rule for when the cheap executor fails.