
M365.FM · 2026-07-26 · 1h 14m
Many AI agents start out fast, responsive, and surprisingly intelligent. But after a few months of real-world use, something changes. Response times increase, costs rise, prompts become enormous, and accuracy begins to decline. Organizations often respond by upgrading to larger models, expanding prompts, or adding more orchestration - but the underlying problem remains. The issue isn't the model. It's the architecture. This episode explains why monolithic prompts create what is known as the Context Tax, how modular Skills solve the problem through progressive disclosure, and why Skills are becoming the architectural foundation of modern AI agents across Microsoft Copilot Studio, GitHub Copilot, Claude Code, and the broader enterprise AI ecosystem. THE CONTEXT TAX Every enterprise AI project eventually faces the same challenge. At first, an agent contains a relatively small system prompt describing its role, tone, business rules, and guardrails. As the organization grows, more instructions are added: Policies Compliance rules Business procedures Examples Edge cases Department-specific workflows Eventually the prompt becomes thousands of tokens long.
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