How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma
Figma’s cautious agent adopters expose missing safeguards, not resistance. Use their feedback to improve verification, plans, review size, and the labeling of AI-generated work.
Figma reports uneven agent adoption and says experienced engineers may be slowest because they see codebase risks first. One detailed plan turned an estimated **six weeks of coding into about one week**, including review.
Make verification executable before implementation: use TDD, encode repeated checks deterministically, and split plans into independently validated, reviewable phases. Put the reason for the change first, and use skeptical maintainers’ feedback as the safety roadmap.
Figma reports uneven agent adoption and says experienced engineers may be slowest because they see codebase risks first. One detailed plan turned an estimated **six weeks of coding into about one week**, including review. Make verification executable before implementation: use TDD, encode repeated checks deterministically, and split plans into independently validated, reviewable phases. Put the reason for the change first, and use skeptical maintainers’ feedback as the safety roadmap. The workflow is still evolving and depends on planning and review effort that raw generation metrics can hide. AI also increases communication volume, so clearly separate human-written intent from generated descriptions to protect scarce reviewer attention.
This confirms that coding-agent adoption is primarily a verification and workflow-design problem, not a raw generation problem. It adds an organizational consequence: skeptical experienced maintainers can identify the controls needed for safe rollout. The reported acceleration remains local and includes planning and review, while increased generated communication can consume the reviewer capacity automation is meant to save.