The Problem: Agents Lost in the Wild West
Picture this: your AI coding assistants are like new developers joining your team, except they don't ask clarifying questions. They just dive in, hunt through your entire codebase, and make their best guess about where things belong. Before our recent code quality investments, that's exactly what was happening to us.
Our agents would stumble around a sprawling codebase, picking random components, hardcoding hex values, and generally making a mess that required constant human cleanup. We realized something had to change, not just for us, but for our AI-powered workflow.
The Monorepo Revolution: Clear Boundaries, Happy Agents
We made the leap to a monorepo architecture using pnpm + Turborepo, breaking everything into scoped packages across 6 applications. The transformation was remarkable.
Before: The Great Hunt
- Agents would search the entire codebase trying to figure out where functionality lived
- No clear boundaries between different domains
- Dependency chaos that could break builds unexpectedly
After: Crystal Clear Organization
- Need to touch post logic? It's in
@waldium/posts - Need storage functionality? Check
@waldium/storage - Turborepo handles the build graph so agents can't accidentally break dependency order
The technical benefits were immediate. With pnpm's singleton dependency management, we saw faster installations and reduced disk usage. Turborepo's intelligent caching and parallelization delivered up to 10x faster build pipelines, giving us those crucial fast feedback loops that keep development momentum high.
But the real win? Package boundaries became our agents' GPS system. Instead of wandering aimlessly, they now know exactly where to go for specific functionality.
Documentation That Lives Where It Matters
We took another bold step: colocating our docs and wiki within the monorepo itself. We built an MDX docs site and a Markdoc internal wiki sitting right next to our app code in the same repository.
Why does this matter for AI agents? They can actually see our architecture decisions and conventions because they're in adjacent directories, not buried in some external Notion page they can't access. Our agents now reference our coding standards, architectural decisions, and best practices as easily as they reference our actual code.
The Design System Game Changer
Nothing illustrated the "convention over inference" principle better than consolidating our design system. Previously, we had:
- Two separate UI primitive libraries doing roughly the same thing
- Agents picking whichever component they found first
- Hardcoded hex values scattered throughout the codebase
- Inconsistent styling that required manual cleanup
After consolidating into one token-based design system, the transformation was immediate. Our agents started getting styling right on the first try because there's only one set of tokens to reference. No more choice paralysis, no more random color picking.
The Key Insight: Conventions Over Inference
Here's the pattern that changed everything for us: agents are way better at following conventions than inferring them.
Think about it. A human developer can look at messy code, understand the implicit patterns, and adapt accordingly. AI agents? They need explicit structure. They thrive on consistency and clear boundaries.
Our code quality investments weren't just good engineering practices. They became the API surface we're giving to our agents:
- Clean package boundaries = clear functional domains
- Colocated documentation = accessible context and conventions
- Unified design system = consistent styling choices
- Well-defined build processes = reliable deployment patterns
The ROI: Less Babysitting, More Building
The return on investment has been substantial. Instead of spending time cleaning up after our AI assistants, we're spending time on actual feature development. Our agents now:
- Navigate to the right packages automatically
- Reference our design tokens consistently
- Follow our documented conventions
- Respect dependency boundaries
The cleaner our codebase became, the less we needed to babysit our AI tools. It's a virtuous cycle: better code architecture enables better AI assistance, which enables faster, cleaner development.
The Future is Agent-Friendly Architecture
As AI coding assistants become more prevalent, we need to think differently about code organization. It's not just about human readability anymore (though that remains crucial). We're designing for a new kind of developer that processes information differently than we do.
The investments we made in monorepo organization, colocated documentation, and unified design systems weren't just technical improvements. They were strategic moves toward a future where AI and human developers work seamlessly together.
The bottom line? Treat your code architecture as an API for agents. The cleaner and more explicit you make it, the more your AI assistants will feel like productive team members rather than chaotic code generators.
