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Drawing a parallel between compiler output and AI agent output, this piece argues that the real problem with lights-out codebases isn't the volume of AI-generated code — it's that we haven't built the verification infrastructure to make trusting that output reasonable. Just as we trust compilers not blindly but through surrounding apparatus (type systems, test suites, monitoring, rollback), we need equivalent upstream formal specifications, comprehensive AI-checks-AI pipelines, and mature production instrumentation for agent-generated code. The post frames the lights-out codebase not as something to fear or accept passively, but as a design target that reveals exactly what engineering infrastructure still needs to be built.

Treat Agent Output Like Compiler Output

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Nice read, i loved it, also in addition, unlike agents, compilers use a predefined pattern(<a href="https://skiplabs.io/blog/codegen_as_compiler?ref=dailydev#nobody-reviews-compiler-output" target="_blank" rel="noopener nofollow">https://skiplabs.io/blog/codegen_as_compiler?ref=dailydev#nobody-reviews-compiler-output</a>) unlike llms so there is no need to review its output since the margin of errors is low, but also the volume of code that is shipped in this agentic era is making code review very scary… but i get the point

By the time you put enough guardrails, parameters, restraints, layers and verifications to make these models trustworthy…you’ll have re-invented the compiler in a circuitous fashion.
IMO, it feels like we’ve basically accelerated the parts of development that didn’t necessarily need accelerating because they weren’t much of a bottleneck, and put unnecessary pressure on the parts that are critical, with these models only making that process more convoluted, rather than less.

The big difference is that the Compiler is Deterministic, once a Compiler is generally trusted verification is obsolete (unless you’re working on the compiler itself or relying on edge cases). An AI-Agent, no matter how good and trustworthy, interprets the tasks and produces tailored output that needs continuous oversight.

To review code properly key is to understand what it does.