Building Modern Software with AI Agents: Valuable Lessons And Anti-Patterns

We built a university-grade, professor-validated AI grading platform on top of Go Lang microservices running in Google Cloud, co-designed with partner universities and independent professors — all in four months with one product dev lead (myself), one full-time developer, three interns, and an army of AI agents.

Context

I took on a contract assignment with GOMYCODE, a coding school in MENA, to build GOMYTEACHER — a standalone AI-native grading assistant for external university professors. The company had already built an LLM-enabled grading engine for their internal use, and they wanted to take it to market as a standalone product.

The constraints: One full-time developer headcount, and limited tech spending budget. The company's legacy .NET learning management system, and we still needed to evolve their new Python grading APIs to meet university-level standards and production requirements.

The opportunity: Leverage the company's existing network of universities for design partner validation, work with an experienced AI product strategy advisor, and tackle the problem of professors spending countless hours grading assignments manually.

The Execution

Technical Architecture Decisions

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Product & Process Setup

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The AI Integration Strategy

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Lessons Learned

1. Control: Verify Everything, Trust Nothing Blindly

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2. Less Is More: Fight Over-Engineering at Every Turn

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3. Specs: Write Human-Clear Specs, Let AI Generate Code

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4. Tooling: Build Tools That Make AI More Productive

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5. Stack Selection: Choose for the Problem, Not the Resume

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6. Developer Skills: Domain Knowledge Trumps Framework Expertise

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7. Team Leverage: AI as the Great Leveler

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The Results

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Reflection & What's Next

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Key Takeaways

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