The design gap
Why do most AI delegations
underperform?
Claude Cowork is powerful. It can execute complex, multi-step tasks autonomously. But the quality of its output depends entirely on the quality of its input — the task description you provide.
Most teams write ad hoc prompts. They describe what they want in natural language, iterate when results disappoint, and eventually build a library of "prompts that work" through trial and error. This is prompt engineering — and it doesn't scale.
The alternative is step-level specification. Before you delegate a task to Cowork, you specify exactly what it should accomplish (Work Plane), how it should execute (Execution Plane), and what the result should feel like to the stakeholder (Experience Plane).
Henry builds these specifications through guided conversation, using the AAAERRR methodology from Deliberate Work to decide which parts of your operation belong to a human, to Cowork, or to a hybrid. The output is a structured task description that Cowork can execute reliably, repeatably, and at the quality bar you defined — not the quality bar you hoped for. For AI-ready teams and founders delegating first AI work, this is the layer that turns prompt engineering into operational design.
Without specification
Ad hoc prompts. Variable quality. No way to know if the output meets the bar until you review it. Every delegation is a new experiment.
With Henry
Three-plane specifications. Defined inputs, outputs, quality criteria, and execution mode. Every delegation follows the specification — and the specification is the accountability.