{"slug": "ai-is-changing-what-correct-software-means", "title": "AI Is Changing What 'Correct Software' Means", "summary": "A developer argues that AI-generated code is breaking the traditional definition of correct software, where green CI pipelines and passing unit tests no longer guarantee semantic correctness. The developer proposes shifting from output equality assertions to system invariants and architectural fitness functions, and demonstrates a verification pipeline using NetArchTest and Semantic Kernel to enforce runtime guardrails on AI-generated code.", "body_md": "Software engineers used to define a \"Correct software\" via a simple deterministic binary an *Expected Output*.\n\nAll Test are passed, but this is collapsing, a green CI pipeline no longer guarantees correct software, with AI-generated code that's look syntactically perfect and test-compliant yet they are semantically wrong in a ways that appears in productions.\n\nThe bottleneck in software engineering is no longer writing code, it is verifying it execution intent\n\nTraditional software used to relies on deterministic assertions. We write code with known paths, control inputs, and verify exact results:\n\n```\n[Fact]\npublic void CalculateDiscount_StandardUser_ReturnsTenPercent()\n{\n    var calculator = new DiscountCalculator();\n    var result = calculator.GetDiscount(UserType.Standard, orderTotal: 100);\n    Assert.Equal(10, result);\n}\n```\n\nThis works well because we write the logics and most bugs comes from missed edge cases or small mistakes.\n\nBut with AI-generated codes, it follows a patterns based on trained context and user prompt. An agent will write code that pass 100% of our unit tests but silently violation non-functional, implicit system boundaries:\n\nSo we got a syntactically valid and test passing code but **architecturally incorrect.**\n\nAI agents are exceptionally good at writing tests for the very code they just generated. If an agent writes a flawed implementation, it generates an equally flawed, matching assertion suite.\n\nWhen tests mirror the assumptions of the generation engine, unit tests become a confirmation bias machine.\n\nTo govern AI-generated code, we must shift our definition of correctness from **Output Equality** (`Assert.Equal`\n\n) to **System Invariants** (`Constraint Validation at Execution Runtime`\n\n).\n\nIn an AI-native engineering workflow, correctness is defined by **bounded runtime guardrails** and **architectural fitness functions**.\n\nInstead of testing whether a function returned `10`\n\n, a verification engine continuously enforces structural constraints across the entire codebase execution model.\n\nUsing this we are not just checking test results we are enforcing rules at runtime and checking that the system design stays valid.\n\nLet's build a practical implementation of an architectural verification pipeline.\n\nInstead of trusting AI-generated tests we will enforce rules that our system must always follow:\n\n```\nusing NetArchTest.Rules;\nusing Xunit;\n\npublic class ArchitectureVerificationTests\n{\n    [Fact]\n    public void DomainEntities_MustBeImmutable_AndNeverBypassedByAgents()\n    {\n        // Enforce that AI-generated code cannot introduce mutable state into the Core Domain\n        var result = Types.InCurrentDomain()\n            .That()\n            .ResideInNamespace(\"OrderSystem.Domain\")\n            .Should()\n            .BeImmutable()\n            .GetResult();\n\n        Assert.True(result.IsSuccessful, \"AI generated mutable entities in the domain layer!\");\n    }\n\n    [Fact]\n    public void Handlers_MustEnforceIdempotencyDecorator()\n    {\n        // Ensure every generated Command Handler implements IIdempotentCommand\n        var result = Types.InCurrentDomain()\n            .That()\n            .HaveNameEndingWith(\"CommandHandler\")\n            .Should()\n            .ImplementInterface(typeof(IIdempotentCommand))\n            .GetResult();\n\n        Assert.True(result.IsSuccessful, \"AI generated a command handler lacking explicit idempotency execution!\");\n    }\n}\n```\n\nFor AI agent workflows running in production or ambient background tasks when it tries to change code or data, we validate its intent before allowing it so we ensure:\n\n```\nusing Microsoft.SemanticKernel;\nusing Microsoft.Extensions.Logging;\n\npublic record ExecutionIntent(string TaskDescription, string TargetNamespace, string ProposedDiff);\npublic record VerificationResult(bool IsValid, string StructuralDivergenceReason);\n\npublic class AgentVerificationEngine\n{\n    private readonly Kernel _kernel;\n    private readonly ILogger<AgentVerificationEngine> _logger;\n\n    public AgentVerificationEngine(Kernel kernel, ILogger<AgentVerificationEngine> logger)\n    {\n        _kernel = kernel;\n        _logger = logger;\n    }\n\n    public async Task<VerificationResult> VerifyAgentActionAsync(ExecutionIntent intent)\n    {\n        // Define hard system invariants that the LLM engine cannot negotiate\n        var verificationPrompt = \"\"\"\n            You are an Architectural Verification Engine. Evaluate the proposed code change against the system invariants:\n\n            SYSTEM INVARIANTS:\n            1. No direct database access outside Infrastructure/Repositories.\n            2. Memory allocations on critical paths must avoid heap overhead (no boxing, use ReadOnlySpan<T> where applicable).\n            3. All external state modifications MUST emit an IntegrationEvent.\n\n            PROPOSED CHANGE:\n            Target: {{$target}}\n            Diff: {{$diff}}\n\n            Respond ONLY in JSON format:\n            {\"is_valid\": true|false, \"reason\": \"Detailed failure justification\"}\n            \"\"\";\n\n        var arguments = new KernelArguments\n        {\n            [\"target\"] = intent.TargetNamespace,\n            [\"diff\"] = intent.ProposedDiff\n        };\n\n        var result = await _kernel.InvokePromptAsync(verificationPrompt, arguments);\n        var responseJson = result.GetValue<string>();\n\n        // System parses verification evaluation deterministically\n        return System.Text.Json.JsonSerializer.Deserialize<VerificationResult>(responseJson)!;\n    }\n}\n```\n\nDeploy this verification process directly inside your **Azure DevOps Pipeline** or **GitHub Actions Engine** alongside Azure Container Apps for isolation:\n\nMoving to an invariant-driven verification architecture introduces real engineering trade-offs:\n\nAI is not removing the need for software engineering — **it is raising the level of abstraction.**\n\nWhen writing code becomes frictionless, the primary value of a senior engineer shifts from *syntax authoring* to *system boundary definition*. Stop relying solely on traditional unit tests to validate AI-generated code. Build verification engines that enforce immutable architecture invariants, audit non-deterministic outputs, and protect runtime stability.\n\n**Software correctness is no longer about passing unit tests. It is about proving your system cannot violate its core invariants.**\n\n*How are you adapting your CI/CD pipelines to handle AI-generated code? Are you relying on static unit testing, or building verification boundaries? Drop your thoughts in the comments below!*", "url": "https://wpnews.pro/news/ai-is-changing-what-correct-software-means", "canonical_source": "https://dev.to/majdizlitni/ai-is-changing-what-correct-software-means-14hf", "published_at": "2026-08-22 16:41:58+00:00", "updated_at": "2026-08-22 17:13:47.918999+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "ai-safety", "developer-tools"], "entities": ["NetArchTest", "Semantic Kernel", "Xunit"], "alternates": {"html": "https://wpnews.pro/news/ai-is-changing-what-correct-software-means", "markdown": "https://wpnews.pro/news/ai-is-changing-what-correct-software-means.md", "text": "https://wpnews.pro/news/ai-is-changing-what-correct-software-means.txt", "jsonld": "https://wpnews.pro/news/ai-is-changing-what-correct-software-means.jsonld"}}