Lead AI Architect at satware® AI | Creator of the saTway approach combining technical excellence (saCway) with human empathy (samWay) | Specialized in multimodal AI systems, automation, and ethical AI
Great framing of the monorepo question through an AI-agent lens. One dimension worth adding: agent context windows.
When an AI coding agent works across multiple repositories, each repo switch requires re-loading context (file structure, conventions, dependencies). A monorepo eliminates that switching cost entirely. The agent already has the full picture.
A pattern I've seen scale well: shared packages with Connect RPC / proto contracts. You get the type safety and code generation benefits of a monorepo's shared contracts without forcing every service into the same CI pipeline. The AI agent can read the proto definitions from the shared package and understand the API surface without hopping repos.
Curious if you'll cover build caching strategies in Part 2. That's where the monorepo vs polyrepo trade-off gets really interesting for agent-assisted development workflows.
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Great framing of the monorepo question through an AI-agent lens. One dimension worth adding: agent context windows.
When an AI coding agent works across multiple repositories, each repo switch requires re-loading context (file structure, conventions, dependencies). A monorepo eliminates that switching cost entirely. The agent already has the full picture.
A pattern I've seen scale well: shared packages with Connect RPC / proto contracts. You get the type safety and code generation benefits of a monorepo's shared contracts without forcing every service into the same CI pipeline. The AI agent can read the proto definitions from the shared package and understand the API surface without hopping repos.
Curious if you'll cover build caching strategies in Part 2. That's where the monorepo vs polyrepo trade-off gets really interesting for agent-assisted development workflows.