Your AI Agents Need You to Write Everything Down

Effective AI agents require a well-maintained context layer that captures not only accessible data but also the undocumented institutional knowledge, workflows, definitions, decision rules, and relationships among systems that employees use intuitively. The piece argues that agents can fail or make harmful decisions when they lack specific process context, such as approval requirements, customer-priority rules, legal retention obligations, or distinctions between similarly named business entities across databases. It groups business context into technical metadata, business definitions and metrics, and process knowledge, noting that the latter two often require substantial human documentation efforts because they may exist only in employees’ experience. Citing a survey in which only 7% of enterprises considered their data fully ready for autonomous agents, it contends that improving data access alone is insufficient when critical information has never been recorded as usable data. Organizations are therefore encouraged to make context engineering an ongoing practice, documenting evolving workflows, the rationale behind metrics and classifications, and cross-source data connections so agents can act more reliably within appropriate guardrails.

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