Notes from inside the harness.
What we're learning building AI for electronics engineering — where it breaks, why it breaks, and what it takes to make it hold. Written for engineers, not for the algorithm.
Why AI hasn't worked for electronics engineering (yet)
An honest post-mortem: text-first models meeting binary ECAD formats, no verification loop, invented part numbers, and a cost of failure measured in respins — and what it actually takes to close the gap.
Why you need a harness, not a chatbot
The case for wrapping AI in deterministic checks, native ECAD access, live component data, and Git-based review — and what goes wrong with every architecture that skips a layer.
The fragmented electronics ecosystem is the real AI bottleneck
ECAD silos, PLM islands, datasheet PDFs, distributor portals, MCAD walls: why electronics data fragmentation starves AI of context, why exchange formats didn't fix it, and what a digital thread actually requires.
Agent concepts electronics engineers actually need to know
A working vocabulary in EE terms: what an agent actually is, why tool calls beat model memory, what a context window means for your 400-page datasheet, and where determinism must take over from judgment.
How to build an internal AI program for electronics engineering (and why it's so hard)
For engineering leaders: why pilots stall, the five problems you'll actually face (data, integration, verification, staffing, maintenance), a staged roadmap, and the build-vs-buy math nobody puts in the pitch deck.
See the harness on your own designs.
Bring a schematic. We'll show you what agents, deterministic checks, and Git-based review look like on a real board.
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