⚙️ TL;DR: without touching the model, automatically improving the software that runs the agent cuts token cost by roughly half. Title: SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness URL: 📌 Highlights 🔍 Searched ~150 directions across ~500 environments, running 3,000+ trials to discover efficiency mechanisms 🧩 4 mechanisms found, including Action Fusion, which merges a file edit and its test run into one request 📉 On EdgeBench: 49.0% less token traffic, 33.2% lower cost, at 93.7% of baseline performance 🔄 Applied to Opus 5 with zero extra tuning, still keeps 44.7% token and 33.5% cost reduction 💰 On Terminal-Bench 4, cost per solved task drops 11.6% ⏱ Estimated $8.75-13.50/hour savings versus native Codex The interesting part: optimizing the harness instead of the model turns out to be a genuinely production-relevant efficiency lever. #AIAgents# #LLMCostOptimization#

💰 Same model, same task, but swap the software wrapper (harness) around it and the cost can jump 5x while success rate barely moves. Title: HarnessTax: How Much Does the Harness Matter for Coding Agents? URL: UC Berkeley and Arena compared three coding agent harnesses, Claude Code, Codex CLI, and Pi, across 7 models and 21 model-harness pairs. Here are 3 highlights. 📊 Statistically, harness barely moves success rate Out of 42 within-model harness comparisons, only 1 was statistically significant (chance alone predicts about 2), and none survived correction for multiple comparisons. 💸 Cost can differ 5x at the same success rate On GPT-5.6 Luna, Claude Code cost $0.15/task at 55.6% success, while Pi cost $0.03/task at 53.3% success, nearly identical accuracy, 5x the price. 🪶 The minimal open-source harness holds its own In 9 of 12 model comparisons, a non-vendor harness (Pi or Codex CLI) achieved the top success rate, showing a stripped-down harness can genuinely compete. The practical takeaway: prioritize cost and reliability over flashy add-on features first. #CodingAgents# #LLMCostOptimization#