// technical debt

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Cheap AI Is Creating Unmaintainable Agentic Systems

When the cost of adding another AI component approaches zero, teams stop architecting and start accumulating—layering agents, tools, and integrations without rationalizing their interactions or dependencies. The result mirrors the "big ball of mud" antipattern from software engineering: systems that work until they don't, where no one fully understands the failure surface and small changes cascade unpredictably. The underlying problem is economic, not technical. The frictionless cost of AI iteration has outpaced the discipline required to keep complex multi-agent systems coherent.

Notre Dame's infrastructure overhaul reveals the real cost of AI readiness

Notre Dame's decision to systematically replace aging network infrastructure before deploying AI exposes a gap between public narrative and operational reality: most organizations lack the underlying systems to run advanced AI workloads, but won't say so. The "AI-ready" label obscures what actually happens first—enterprises strip out decades of technical debt by replacing legacy hardware, consolidating data silos, modernizing security—before they can safely integrate AI. The bottleneck is unglamorous infrastructure work that requires sustained investment with no immediate competitive payoff, not innovation itself.