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Collaborative design and AI prototyping software

Figma

Figma builds collaborative software for designing digital products. Figma Design gives designers and product teams a shared canvas for creating interfaces, reviewing work and maintaining reusable components, styles and variables. Its AI agent can generate design directions and edit text and images. Figma Make turns prompts and design context into code-backed, interactive prototypes that users can refine through visual editing, code or further prompts.

Dylan Field and Evan Wallace founded Figma in 2012, building design tools for the web; Field leads the company as CEO. Its integration with AI coding workflows carries design information into implementation: the Figma MCP server lets agents retrieve components, variables and layout data, generate code from selected frames, and create or modify native Figma content. Code Connect links design-system components to their codebase implementations so generated code can reuse actual components.

Figma and Adobe ended their proposed acquisition in 2023 after concluding that regulatory approval was unattainable, leaving Figma independent. Figma went public in 2025. Its reach extends across large organizations: the company reported that 95% of the Fortune 500 used Figma as of March 2025.

Explore the recordings

This Figma archive offers two complementary paths: building an agent-facing integration and making coding agents useful inside an engineering organization. The guide reflects the supplied recordings, including the adoption talk addition. Statements about Figma’s practices and implementation choices describe what speakers reported in these recordings; they do not establish current product status.

Build an integration: design context, evaluations, and launch choices

Start with Jesse Lumarie’s Figma MCP server talk for a concrete account of moving from a one-day-a-week experiment to a launch in about three months. Follow the decisions around representing design context, reusing existing code components, evaluating results, and accommodating uneven MCP client support. Lumarie describes launching locally before building a remote server; read that as the recorded launch sequence, not a statement about today’s server availability.

Adopt coding agents: verification, planning, and trust

Start with Eyal Blum’s organizational adoption talk if your challenge is getting beyond early agent successes on small tasks. Blum describes trust breaking down on larger production problems and recommends deterministic verification, red-to-green TDD, and plans divided into reviewable phases with validation gates. His account also covers uneven adoption, experienced engineers’ quality-control burden, reduced developer agency, and verbose AI-assisted communication. Treat these as his recorded observations and recommendations. His estimated 5× speedup comes with an unreconciled timing breakdown, so it should not serve as a reliable planning benchmark.

Read together: evaluate the output and organize the work

For a broader engineering path, watch the MCP launch account followed by the coding-agent adoption account. Compare Lumarie’s automated evaluations and integration constraints with Blum’s emphasis on tests, planning, and human review. This pairing connects a specific integration project with organizational workflow questions; it does not establish that both speakers describe one shared initiative or a single company-wide policy.

2 talks

Newest first

2 speakers at AIE

Affiliations reflect their AIE appearances, not necessarily current employment.

Company sources · checked 2026-09-01