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This article analyzes how architectural choices in agent harnesses, rather than model selection alone, fundamentally impact performance and provider economics for AI coding agents. It details three distinct execution modes—terminal UI, API-based, and file-system—that dictate latency profiles and require specific inference strategies to maintain loop integrity.

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Decoding AI’s Open-Source Course Maps Three Ways to Run an Agent Loop and the Provider Economics Behind Each

MarkTechPostAugust 22, 20265 min read

Most teams treat ‘which model’ as the important decision. The harness engineering literature keeps pointing somewhere else. In LangChain’s Terminal-Bench experiment, changing only the harness—same model throughout—moved a coding agent from roughly 30th place into the top 5. That result reframes the question. If the harness decides quality, then how you run the loop becomes […] The post Decoding AI’s Open-Source Course Maps Three Ways to Run an Agent Loop and the Provider Economics Behind Each appeared first on MarkTechPost.

Most teams treat ‘which model’ as the important decision. The harness engineering literature keeps pointing somewhere else. In LangChain’s Terminal-Bench experiment, changing only the harness—same model throughout—moved a coding agent from roughly 30th place into the top 5. That result reframes the question. If the harness decides quality, then how you run the loop becomes an architecture decision, not a deployment detail. Paul Iusztin’s open-source course Building a Coding Agent From Scratch builds a Python agent called Decode. Published through Decoding AI, it separates three run modes.…

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