A five-stage plan to build the context layer that lets AI reason from Aero — not just mimic how it looks. The diagnosis, the sign-off, and the work in progress.
After seven years building Aero, I could finally name the root cause of every adoption failure: the system was never documented in a way anyone — human or AI — could reason from. Not just incomplete. Wrong in kind.
AI tools mimic how things look, not how they're built. They produce output that resembles Aero without understanding its structure, its decisions, or its constraints. A component generated by an AI isn't wrong because it looks different. It's wrong because it doesn't know what it's for.
[ Fill in: The specific incidents or patterns that led you to this diagnosis. What adoption looked like before. What "reasoning from the system" would have prevented. ]
The plan isn't about adding AI features to the design system. It's about building the infrastructure layer the system always should have had — and making it machine-readable along the way.
Structured semantic token documentation. Every token gets a name, a role, a usage rule, and a decision rationale.
Figma and code linked. A single source of truth where token values flow both directions without manual sync.
Component documentation fused with usage context, design decisions, and real production examples.
System health monitoring. Coverage metrics, adoption signals, drift detection across products.
AI can query, reason from, and build with the system. MCP layer enables agents to use Aero as infrastructure.
[ Fill in: What 110 semantic tokens means in practice. How you structured the documentation — what each token entry contains. The difference between a token catalog and a context layer. ]
[ Fill in: How you captured the why behind each token. What format you used. How this differs from what existed before. ]
[ Fill in: How you made the case internally. What the proposal looked like. What objections you addressed. ]
The end state isn't an AI that can generate a component that looks like Aero. It's an AI that knows Aero well enough to reason about which component to use, why, and whether the thing it's building is consistent with the system's decisions.
The MCP (Model Context Protocol) layer is the engineering interface that makes this possible — a structured API into the design system that agents can query at runtime.
[ Fill in: What the MCP layer proposal looks like. What an agent can ask it. What you prototyped in the mini Aero dashboard. ]
[ Fill in: Stage 2 timeline and what it requires. What you're building now. What you need from engineering to move forward. ]
[ Fill in: The broader vision — what Aero looks like when all 5 stages are complete. What changes about how SpotOn builds product. ]
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