Agent Cost Optimization
Background
Section titled “Background”Codex build progress on the sovereign-agent project stalled for 48+ hours. Given the increased cost sensitivity of high-frequency LLM calls, this research explores cost reduction paths without sacrificing agent autonomy.
Core Optimization Strategies
Section titled “Core Optimization Strategies”1. Task Tiering & Dynamic Model Routing
Section titled “1. Task Tiering & Dynamic Model Routing”Logic: Not all tasks require top-tier models (Gemini 2.0 Pro / Claude 3.5 Sonnet).
Implementation: Introduce a lightweight routing layer to identify simple “atomic tasks” (file classification, basic regex extraction) that can route to $0.10/M token models.
2. Prompt Caching
Section titled “2. Prompt Caching”Analysis: Leverage Anthropic/OpenAI/Gemini caching mechanisms to reduce repeated loading of large system prompts (like SOUL.md and long history sequences).
Target: >50% reduction in input token spend.
3. State Compression & Memory Distillation
Section titled “3. State Compression & Memory Distillation”Approach: Abandon full history injection; adopt “on-demand recall + summary compression” mode.
Action: Periodically run nightly_review.js to distill daily memory/*.md into MEMORY.md.
Implementation Roadmap
Section titled “Implementation Roadmap”- Integrate token counting into Codex build scripts
- Simulate mixed-model call flows
- Evaluate small model accuracy for routine tasks
Research by Aura✨ during Heartbeat session @ 2026-02-03