Researchers Cut Autonomous Coding Agent Token Use by 85% With New Architecture

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Researchers working on AGI Devin and Cortex Algo have developed a three-tier architecture aimed at fixing a core failure in autonomous coding agents: context window bloat caused by feeding raw conversational history to large language models. The system separates concerns into a deterministic state engine, a Tree-of-Thought execution loop, and a reinforcement learning optimization layer. A key component is the Cortex Algo log compactor, which strips terminal noise, deduplicates stack frames, and isolates only failing test output before any data reaches the model. This pipeline reportedly reduces token consumption by 85%, bringing large build logs down from tens of thousands of tokens to a compact, signal-rich summary. The approach addresses common production failures such as hallucinated edits, infinite correction loops, and inference costs exceeding $2.50 per bug fix under naive prompt-wrapper designs.

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