Our first commit was “Hello World.” A few months and hundreds of commits later, we had a production-grade agent platform with a comprehensive test suite. Here’s how we got there without drowning in complexity.

The Growth Curve

Development moved in clear, recognizable phases: a single agent running a single tool, then Kubernetes deployment, then multi-agent orchestration, then a hardening pass focused on production security, then enterprise integrations, then a stable platform. Each phase added real surface area without any single part of the codebase growing unmanageably large — because the boundaries between domains were clear from day one.

The Rules That Scaled

We adopted strict coding standards in week one, before the codebase was large enough to need them. This felt premature. It wasn’t.

The specifics don’t matter for this post — what mattered was consistency at scale:

  • One way to call things — same parameter patterns everywhere so engineers never guess interface shape.
  • Test doubles you can trust — generated fakes that break at compile time when contracts change, not hand-rolled mocks that drift.
  • Methods over free functions — dependency injection by default so production and tests share the same seams.
  • Flat control flow — guard clauses instead of nested branches; readability compounds as files grow.
  • Small public APIs — export only what other packages need; everything else stays private.

Why this matters for agents: agent platforms accrue integrations faster than typical CRUD apps. Without mechanical consistency, every new tool provider becomes a one-off. With it, a senior engineer can review a pull request in minutes because the shape is familiar even when the domain is new.

Domain Boundaries, Not Layer Soup

The codebase is organized around clear domain boundaries rather than technical layers — data access, application services, tool providers, identity, observability, and so on each live in their own space, with dependencies flowing in one direction only. Lower-level packages never import from higher-level orchestration code; the compiler’s import-cycle detection enforces this automatically, which turns a design intention into something that’s actually impossible to violate by accident.

The Test Pyramid

Most of the test suite is fast, isolated unit tests built on generated fakes. A smaller slice is integration tests that compile and execute full, composed workflows against a mock model — these are disproportionately valuable, because they catch bugs that only exist at the level of the composed system (see the ReAcTree bugs post for concrete examples). A final, small slice is manual acceptance testing against real user-facing scenarios.

Every change runs linting, formatting, and the full test suite before it can merge. No exceptions, no “we’ll fix it later.”

Patterns That Emerged

A composable middleware chain for tool execution — the same idea as HTTP middleware, applied to AI tool calls. Each concern (logging, safety checks, rate limiting, and so on) is its own small, independently testable unit that wraps the next one in the chain. Adding a new concern means writing one function and inserting it into the chain — no framework, no reflection, no magic.

A provider pattern for every external integration — model providers, vector stores, document parsers, messaging platforms all implement the same small interface and get registered by name. Swapping an implementation becomes a matter of changing configuration, not code.

A standard pattern for parallel work — every place we run independent operations concurrently uses the same structured-concurrency approach with proper error propagation and safe shared-state handling, rather than ad-hoc goroutine management. One pattern, everywhere, rather than a different flavor of concurrency bug in every package that needed to run things in parallel.

What We’d Do Differently

  1. Start with integration tests earlier. We wrote unit tests from day one but didn’t add integration tests testing fully composed workflows until well into the project. Some bugs lived in exactly that gap.

  2. Smaller pull requests. Our early PRs were large. We eventually learned to keep them small — smaller PRs get meaningfully better reviews.

  3. Document package boundaries explicitly. We relied on “everyone just knows” for longer than we should have. A short written explanation of the dependency graph would have helped onboarding a lot.

The Result

The codebase is still fast to work in months later. Adding a new tool provider takes about a day. Adding a new middleware layer takes about an hour. The architecture patterns we chose in week one are still holding, and lint issues are still at zero because the rules were never optional.

Acknowledgments. Deepjyot Kapoor contributed to early platform plumbing and API docs at Aiden.

What architecture patterns does your team enforce from day one? I’m curious about the “premature” rules that turned out to be essential. Find me on GitHub or LinkedIn.

🚀 We’re building AI-powered SRE at StackGen. If you’re tired of 3 AM pages and want AI agents that triage incidents, run diagnostics, and draft RCA reports — check out ai.stackgen.com and try our new SRE offering.