How Save-Game Context Compaction Fixes Spec Drift in Multi-Step Coding Agents (-91.9% Input Tokens)
💡 AI Labo Benchmark Series — Experiment E12 (Ranked #6 out of 41 Patterns) This post is part of our empirical benchmark series testing AI coding agent harness patterns inside network-isolated Proxmox VMs against a hidden 20-case grading suite. 🚀 View All 41 Benchmark Results & Master Ranking 🛡️ Read How Our Proxmox & systemd Anti-Cheating Sandbox Works Metadata & Experiment Identity Experiment…
The AI Labo Benchmark Series tested the AI coding agent harness patterns inside network-isolated Proxmox VMs against a hidden 20-case grading suite. The experiment focused on the Save-Game Context Compaction pattern, which addressed compound issues caused by unbounded history accumulation. In Mode A, the mode without any compaction, the agent suffered from quadratic token inflation and mid-task spec drift, resulting in a 90.0% failure rate in hidden end-to-end tests.
In contrast, Mode B utilized milestone context compaction, reducing input tokens by 91.9% and achieving a 100% pass rate. This improvement translated to a $0.0095 API cost versus $0.0340 in Mode A, representing a 72.2% cost reduction.
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