Moda uses a multi-agent system built on Deep Agents and traced through LangSmith to let non-designers create and iterate on professional-grade visuals.

Langchain is a publication focusing on programming languages, language design, and compiler development. Readers can explore articles covering topics such as language features, syntax design, and compiler optimization techniques. Additionally, they can learn about programming language theory, language implementation challenges, and practical applications of language design principles.

LangChain

Moda is an AI-native design platform for non-designers that uses a multi-agent system to generate and iterate on professional visuals. The architecture consists of three agents: a Design Agent (custom LangGraph loop), a Research Agent, and a Brand Kit Agent (both on Deep Agents). Key engineering decisions include a custom DSL instead of raw coordinate-based scene graphs, a triage-then-skills pattern with prompt caching, dynamic tool loading (12–15 core tools plus ~30 on-demand), and context scaling based on canvas size. LangSmith provides full observability for prompt iteration, cost tracking, cache hit analysis, and error diagnosis. The platform targets B2B teams needing polished, brand-accurate pitch decks quickly.

How Moda Builds Production-Grade AI Design Agents with Deep Agents

The Challenge: Making AI Good at Visual Design

Context Engineering: The Details That Matter