Agentic coding has a problem - variance. What if single-agent runs are leaving performance on the table by design? Due to the stochastic nature of LLMs, each agent run has slight variations. Even with the same context, one session might land near the peak, another somewhere in the middle.

Part 2 of a series on context engineering and building with AI coding agents. Part 1 introduced probability distributions and information architecture. In subsequent pieces, I’ll go into specifics on my workflow, and what I’ve learned from building KOUCAI.

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