TurnCost: Turns, Not Observations: Cache-Aware Loop Engineering for Coding Agents
We analyze 600 publicly released coding-agent trajectories spanning two agent scaffolds and two language models.
ProofPaper ↗
Key points
- AI coding agents operate in iterative loops: they modify code, run tools, inspect the results, and decide what to do next.
- We call the design of this process loop engineering, which includes context management, model selection, compaction, and termination.
- We derive and empirically validate a closed-form break-even condition for model delegation.
- We release TurnCost, a tool for re-scoring trajectory corpora under alternative caching, pricing, compression, and routing policies.
Sources (1)
- [1]TurnCost: Turns, Not Observations: Cache-Aware Loop Engineering for Coding AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 06:03 PM
We analyze 600 publicly released coding-agent trajectories spanning two agent scaffolds and two language models.
AI coding agents operate in iterative loops: they modify code, run tools, inspect the results, and decide what to do next.
Extractive summary: sentences quoted from the sources.
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