An engineer documents a firsthand experiment using multi-agent AI coding teams inside Google's Antigravity Agent Hub to port the Python python-statemachine library into an idiomatic, statically typed Dart package. The workflow assigns strict, tool-enforced roles - Architect, Tester, Coder, and Coordinator - each with restricted read/write access, forming a 'cognitive firewall' that keeps context windows clean and prevents any single agent from altering both tests and implementation. It walks through adapting TDD's Red-Green cycle for a compiled language (using compilation skeletons to satisfy the compiler before real tests can run), and details friction points like accidental deletion of test utilities, Python-to-Dart spread operator mismatches, and replacing dynamic Python patterns like hasattr with idiomatic Dart callback delegates. The resulting package and skill files are shared as a reusable reference rather than a maintained library.
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An example challengeWhat didn't go well: Friction points and mid-flight skill updatesQuestions this post answers
How can I use test-driven development with multiple AI coding agents when writing tests for methods that don't exist yet causes compile errors in a statically typed language like Dart?
Have the coder agent create a compilation skeleton with class and method stubs that throw UnimplementedError before the tester's failing tests can run. Once the skeleton satisfies the compiler, run dart analyze and dart test to confirm tests fail for the right reason (unimplemented logic, not syntax errors), then implement real logic to pass them in the Green phase. daily.dev surfaces workflow write-ups like this for developers refining multi-agent TDD setups.
What agent roles and file permissions work well for a multi-agent AI coding team doing test-driven development?
A four-role setup works: an Architect that writes specs but cannot touch lib/, test/, or example/; a Tester that writes failing tests under test/ but cannot view or write to lib/ or specs/; a Coder that implements code under lib/src/ but cannot edit test/ or specs/; and a Coordinator that manages git commits, runs verification, and delegates tasks without direct code edits. This role separation stops any single agent from altering both tests and implementation. Developers designing agent permission boundaries can track patterns like this via daily.dev.
What problems come up when porting a dynamically typed Python library like python-statemachine to Dart using AI agents?
Three main friction points emerged: agents confusing temporary compilation stub code with real implementations (fixed with explicit comment headers), spread operator type mismatches because Python iterables don't map directly to Dart's Iterable type, and Python's hasattr-based dynamic property checks needing to be replaced with explicit callback delegates for idiomatic, statically typed Dart code. daily.dev helps developers porting dynamic-language libraries stay ahead of type-system gotchas like these.