I Let an AI Agent Build a Feature End-to-End. Here’s What Broke.

2026-07-12 · Source: AI on Medium · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

An experiment demonstrated an AI agent's ability to build a bulk CSV import feature end-to-end in 40 minutes, producing clean code and passing tests for a B2B SaaS admin panel. Despite initial impressions, the feature contained four critical issues: silent file encoding errors with UTF-8 BOM and Latin-1 characters, a semantically empty deduplication test that mocked its own logic, an N+1 query problem causing timeouts for 50,000-row imports, and a multi-tenant data leak vulnerability due to trusting a client-supplied tenant ID. These failures highlight that while agentic tools generate impressive first drafts, they often replicate common junior-to-mid-level engineer mistakes, particularly at edge cases, scale, and security boundaries.

Key takeaway

For AI Engineers or Software Engineers integrating agentic coding tools, you must shift your focus from writing code to rigorously reviewing agent-generated output. Prioritize verifying edge cases, scrutinizing test assertions for semantic emptiness, and validating performance with production-scale data. Explicitly define security and multi-tenancy boundaries in your specifications, as agents will not infer these critical details. This approach ensures that while agents accelerate first drafts, human expertise remains crucial for shipping robust, secure software.

Key insights

AI agents fail like junior engineers, especially at edge cases, scale, and security, requiring human oversight.

Principles

• Agents optimize for correctness on examples, not production scale.
• Green test suites from agents need skeptical review.
• Authorization boundaries must be explicit in specs.

In practice

• Review agent plans for edge cases.
• Verify agent-written tests' assertions.
• Test with production-shaped data.

Topics

• AI Agents
• Software Development
• Code Review
• Data Validation
• Performance Optimization
• Security Vulnerabilities
• Multi-tenancy

Best for: AI Engineer, Software Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI on Medium.