Decoding AI Maps Three Agent Loops and Their Economics

AI-agent performance depends on more than the underlying model. The harness that manages tool calls, state and repeated reasoning can materially affect latency, reliability and the choice of inference provider. Decoding AI uses its open-source Decode project to show why loop design is a core deployment decision, linking software architecture directly to model performance and the economics of operating agents at scale.

Decoding AI’s latest course article compares three approaches to running an agent loop and uses experimental results to examine their latency profiles and economic trade-offs. The report does not specify a publication date or a single dollar cost, but says each architecture changes the number and timing of inference calls. Those differences can alter response times, provider selection and operating expenses, making the harness a key variable in production AI systems.