TrueCourse: Automating Architecture Reviews for AI-Generated Codebases

AI writes code fast. But who checks the architecture?

Developer Mushegh Gevorgyan has launched TrueCourse to address the hidden architectural debt created by AI coding agents. The system utilizes AST-based static analysis to identify circular dependencies and structural violations that human and AI reviewers frequently overlook.

Why This Matters

AI-assisted tools like Cursor and Claude Code prioritize functional code over long-term structural integrity, leading to issues like ‘god modules’ and service coupling. TrueCourse bridges the gap between rapid code generation and sound engineering by providing a deterministic way to audit system-wide architecture and prevent technical debt accumulation.

Key Insights

• TrueCourse combines AST-based static analysis with AI-powered reviews to detect deep semantic issues such as race conditions and security anti-patterns (2026).
• Cross-service flow tracing enables developers to visualize end-to-end request flows across service boundaries automatically.
• The tool supports major JavaScript ORMs including Prisma, TypeORM, and Drizzle to generate ER diagrams and identify missing database indexes.
• A specialized ‘Diff Mode’ compares uncommitted changes against previous analyses to highlight newly introduced architectural violations before they are merged.

Working Examples

Starts a local server with an embedded PostgreSQL database to analyze the codebase and visualize dependencies.

npx truecourse analyze

Compares uncommitted changes against the baseline to detect new architecture violations.

npx truecourse analyze --diff

Practical Applications

• Use case: AI coding agents can consume TrueCourse’s structured CLI output to automatically fix architectural violations during the development cycle.
• Pitfall: Code generated by AI often includes ‘god modules’ with excessive exports; TrueCourse flags these to maintain modularity.
• Use case: CI/CD pipelines can integrate architectural checks to ensure new commits do not introduce circular dependencies or layer violations.
• Pitfall: Semantic errors like empty catch blocks or unsafe random number generation are identified via AI-powered semantic review.

References:

• https://github.com/truecourse-ai/truecourse
• https://www.npmjs.com/package/truecourse
• https://dev.to/mushegh_gevorgyan_8a8c8fa/ai-writes-code-fast-but-who-checks-the-architecture-koi