Decoding AI’s Open-Source Course Maps Three Ways to Run an Agent Loop and the Provider Economics Behind Each
MarkTechPost Michal Sutter
LangChain’s Terminal-Bench experiment found that swapping the agent harness while keeping the same model can move a coding agent from about 30th place to the top 5. The article describes three agent-loop run modes—interactive, remote/offline, and async/online—each with different latency behavior that maps to different inference providers and billing goals. As a result, teams should treat loop execution details as an architecture choice with distinct provider economics, and the open-source Decode framework organizes its agent into a shared core plus three harness shapes.
Why it matters
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.
