AI design agents and coding agents are best at producing options and working code quickly; traditional design tools such as Figma are best at precise, shared, deliberate work on a canvas. Neither replaces the other in a serious product workflow. The practical question is which kind of tool should own each task, and how decisions move between them without getting lost.
By “AI design agents” we mean tools that generate interfaces from prompts (such as Figma Make, v0 or Google Stitch) and coding agents that build UI in a repository (such as Claude Code, Codex or Cursor). By “traditional tools” we mean canvas-based design tools, with Figma the common reference point. Many of these now overlap: Figma has AI features, and agents can write back to Figma.
What changes, and what doesn’t
What changes is the cost of producing something. An agent can build a working screen, or five variants of it, in minutes. What doesn’t change is the cost of deciding what’s right. Choosing between options, holding a product to one point of view, and judging fit for real users still take a designer, which is why Nielsen Norman Group argues that taste becomes the differentiator when production gets cheap.
Comparison by task
Exploring directions
Agents lead. Generating several deliberate directions is cheap, and they’re working code, so you judge them with real behaviour. The risk is variants that differ only in noise; see exploring multiple UI design directions.
Precise visual design
Canvas tools lead. Exact spacing, optical adjustments, illustration and icon work are faster and more controllable by direct manipulation than by prompt.
Design systems
Shared. Variables and components are defined and governed in the design tool, implemented and enforced in code. Agents are good at applying a system and checking conformance, and poor at deciding what the system should be.
Realistic states and content
Agents lead. Building against real data shapes exposes empty, error and long-content cases that static frames tend to skip.
Responsive behaviour
Agents lead, with direction. Code shows real reflow at every width; a canvas shows the widths someone drew. The decisions about what changes per width still need a designer.
Collaboration and review
Canvas tools lead. Commenting on a specific layer, comparing versions side by side and involving non-technical stakeholders are what design tools were built for. Figma’s Code to canvas lets agents bring built UI back into that space; see the Figma and Claude Code round trip.
Production fidelity
Code wins by definition. What ships is the code. Every hour spent perfecting a frame the code won’t match is an hour of drift.
Where agents fall short
- Defaults: without direction they choose the most likely design; see why AI-generated interfaces look generic.
- Global coherence: each session optimises locally; products need consistency across hundreds of screens.
- Judgement: they can compare options well but can’t own the decision.
- Memory: each session starts from zero unless context is supplied.
Where traditional tools fall short
- Distance from production: a frame is a description of software, not software.
- Static states: what isn’t drawn isn’t designed.
- Handoff loss: intent that isn’t in variables, components or annotations rarely survives implementation.
A combined workflow
- Set direction: references, principles and the brief.
- Explore with agents: a few deliberate directions in code.
- Refine the chosen direction where precision is needed, on the canvas or in code.
- Build in the real codebase with a coding agent, against the design system.
- Review built screens, in the browser or captured back into the design tool.
- Feed decisions back into the direction, so the next round starts further ahead.
Step 1 and step 6 are the parts no tool currently owns well. Direction lives in people’s heads, scattered references and chat histories, and every tool in the chain starts without it. That’s the gap we’re building Attuna to fill: in Attuna, in private beta, each project holds its references, rules and preferences, and coding agents read them over MCP. For a broader view of the tools involved, see AI tools for product designers in 2026.