Personal Context vs. Shared Context: A Deep Dive Into How Humans and Organizations Should Feed Their AI Agents

Short summary

Argues that most AI agent failures are context failures, not model failures, and introduces context engineering as the successor to prompt engineering. Distinguishes personal context (accumulated knowledge for one user) from shared context (governed organizational knowledge), covering foundations like context window scarcity, memory types, and context assembly pipelines. Explores approaches for both individual and enterprise scale with honest pros and cons.

• •Most AI failures are context pipeline failures, not model failures
• •Personal context optimizes for one user; shared context governs knowledge across organizations
• •Context engineering replaces prompt engineering — focus on what information reaches the model at decision time

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