{"id":7297,"date":"2026-08-06T03:15:44","date_gmt":"2026-08-06T03:15:44","guid":{"rendered":"https:\/\/www.imt-soft.com\/?p=7297"},"modified":"2026-08-06T03:15:45","modified_gmt":"2026-08-06T03:15:45","slug":"ai-architecture-decisions-catching-hallucinations","status":"publish","type":"post","link":"https:\/\/www.imt-soft.com\/ja\/2026\/08\/06\/ai-architecture-decisions-catching-hallucinations\/","title":{"rendered":"AI Architecture Decisions: Catching Hallucinations"},"content":{"rendered":"<header class=\"Hero c-default tc-white bc-alto bc2-white pt-default pb-default mt-none mb-none bi bp-cc bpm-cc\" style=\"background-image: url('\/wp-content\/themes\/restly-child\/assets\/images\/AI-architecture-decisions\/AI-architecture-decisions-banner.jpg'); position: relative; background-size: cover; background-position: center; z-index: 100;\" alt=\"AI-architecture-decisions-banner\">\n    <div class=\"overlay\" style=\"position: absolute; top: 0; left: 0; width: 100%; height: 100%; background-color: rgba(51, 51, 51, 0.5); z-index: 50;\"><\/div>\n    <div class=\"container\" style=\"position: relative; z-index: 200;\">\n        <div class=\"Hero__inner\">\n            <div class=\"row\">\n                <div class=\"col-lg-8\">\n                    <div class=\"Heading\">\n                        <h1 class=\"Heading__title fs-default\" style=\"text-shadow: 2px 2px 6px rgba(0,0,0,0.7);\">\n\t\t\t\tAI Architecture Decisions: How to Catch Hallucinations \nBefore They Ship\n\n\n\n<\/h1>\n                    <\/div>\n<div class=\"Heading__description fs-s30\">\n                             \n                     \n<\/div>\n                <\/div>\n            <\/div>\n        <\/div>\n    <\/div>\n<\/header>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column pt-5 has-background is-layout-flow wp-block-column-is-layout-flow\" style=\"background-color:#f7f7f7\">\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-center has-background is-layout-flow wp-block-column-is-layout-flow\" style=\"background-color:#f7f7f7\">\n<div class=\"wp-block-columns mb-4 container is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:50%\">\n<p class=\"wp-block-paragraph\"><em><strong>AI shouldn&#8217;t decide your architecture alone.<\/strong><\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That&#8217;s not a warning for junior developers experimenting with a chatbot. It&#8217;s a structural fact about AI-augmented engineering. AI tools are excellent at implementing known patterns. They are unreliable at choosing between them. Architecture is nothing but choosing.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Every AI architecture decision is a judgment call about tradeoffs &#8211; a queue here, a data store there, a trust boundary somewhere else. A model can describe that judgment call fluently. It has no real grasp of what it costs your system six months later.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">You&#8217;re past the &#8220;AI writes my boilerplate&#8221; conversation. The harder question: how do you catch a hallucinated AI architecture decision before five other teams build on top of it?<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:50%\"><div class=\"wp-block-image d-flex  justify-content-center m-3\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"\/wp-content\/themes\/restly-child\/assets\/images\/AI-architecture-decisions\/AI-architecture-decisions.png\" alt=\"AI architecture decisions\"\/><\/figure>\n<\/div><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading container pt-4 pb-3\">Why AI Architecture Decisions Need a Human Check<\/h2>\n\n\n\n<div class=\"container\">\n<div class=\"info-box mt-4 mb-4\">\n  <h3>Quick answer:\n<\/h3>\n  <p>\nAI architecture decisions look identical whether they are right or wrong. The model produces the same confident output either way. Without a validation step, the only thing separating a sound AI architecture decision from a hallucinated one is whether someone with system context happened to catch it.\n <\/p>\n<\/div><\/div>\n<style>\n.info-box {\n\n border-left: 6px solid #2d4f8b !important; \n  background-color: #eef3fb;\n  padding: 15px;\n  font-family: \"Times New Roman\", serif;\n}\n\n.info-box h3 {\n  color: #2d4f8b;\n  font-size: 18px;\n  margin: 0 0 10px 0;\n}\n\n.info-box p {\n  color: #333;\n  font-size: 15px;\n  margin: 0;\n  line-height: 1.5;\n}\n<\/style>\n\n\n\n<p class=\"container wp-block-paragraph\">Code-level hallucinations usually announce themselves. The build fails. A test breaks. A linter complains.<\/p>\n\n\n\n<p class=\"container wp-block-paragraph\">AI architecture decisions don&#8217;t get that safety net. A model can recommend a pattern that compiles fine and passes every test. It can still be the wrong choice for your system. Nothing in the pipeline will flag that for months.<\/p>\n\n\n\n<p class=\"container wp-block-paragraph\">This is the hardest part of AI-augmented engineering to govern. Architecture decisions sit one level removed from the code that implements them. The model isn&#8217;t wrong about syntax &#8211; it&#8217;s wrong about fit. And fit is exactly what a model has the least visibility into. It&#8217;s the same reason AI code review has to extend beyond syntax checks into judgment territory.<\/p>\n\n\n\n<p class=\"container wp-block-paragraph\">There&#8217;s data on how often this kind of confident fabrication happens elsewhere in the stack, and it isn&#8217;t reassuring. Researchers at the University of Texas at San Antonio tested sixteen widely used code-generating models. Roughly one in five recommended packages didn&#8217;t exist at all. The models described libraries with full confidence that had never been built, across 576,000 generated code samples in <a href=\"https:\/\/www.usenix.org\/publications\/loginonline\/we-have-package-you-comprehensive-analysis-package-hallucinations-code\" style=\"color:#0d6efd;\" target=\"_blank\" rel=\"noopener noreferrer\"><u>a USENIX-published study<\/u><\/a>.<\/p>\n\n\n\n<p class=\"container wp-block-paragraph\">If models fabricate at that rate on something as checkable as a package name, expect no better at the architecture level. Whether a pattern fits your team and your constraints is far harder to verify automatically.<\/p>\n\n\n\n<style>\n.atr-container{\nmargin-top:0px;\nmargin-bottom: 0px !important;\n}\n\n.a-container{\nmargin-bottom:10px;\n}\n\n<\/style>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column atr-container has-white-background-color has-background is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-columns container pb-5 pt-5 is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<h2 class=\"wp-block-heading mb-4\">Common Hallucinations in AI Architecture Decisions<\/h2>\n\n\n\n<div>\n<div class=\"info-box mt-4 mb-4\">\n  <h3>Quick answer:\n<\/h3>\n  <p>\n The most common hallucinations in AI architecture decisions are pattern mismatches (recommending a pattern that doesn&#8217;t fit team size or load), fabricated performance guarantees, invented compliance compatibility, phantom or deprecated dependencies, inconsistent paradigm mixing, and context collapse across long design conversations.\n <\/p>\n<\/div><\/div>\n<style>\n.info-box {\n\n border-left: 6px solid #2d4f8b !important; \n  background-color: #eef3fb;\n  padding: 15px;\n  font-family: \"Times New Roman\", serif;\n}\n\n.info-box h3 {\n  color: #2d4f8b;\n  font-size: 18px;\n  margin: 0 0 10px 0;\n}\n\n.info-box p {\n  color: #333;\n  font-size: 15px;\n  margin: 0;\n  line-height: 1.5;\n}\n<\/style>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-columns atr-container is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-center atr-container is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-columns mt-5 is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:50%\">\n<p class=\"wp-block-paragraph\">Most teams making AI architecture decisions have already hit one of these patterns.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pattern mismatch.<\/strong> A model recommends microservices for a five-person team, or event sourcing for a workload that never needed it. The pattern is real. It just doesn&#8217;t match your team size or traffic.<\/li>\n\n\n\n<li><strong>Fabricated non-functional guarantees.<\/strong> &#8220;This design handles ten thousand requests per second,&#8221; stated with total confidence and zero grounding in your actual infrastructure.<\/li>\n\n\n\n<li><strong>Invented compliance compatibility.<\/strong> A model declares a design &#8220;<a href=\"https:\/\/www.imt-soft.com\/ja\/2026\/04\/14\/eu-us-banking-compliance-in-2026-a-bfsi-guide\/\" style=\"color:#0d6efd;\" target=\"_blank\" rel=\"noopener noreferrer\"><u>GDPR<\/u><\/a>-compliant&#8221; with no basis for the claim. In regulated industries, this is one of the most dangerous hallucinations &#8211; it sounds like a fact someone already checked.<\/li>\n\n\n\n<li><strong>Phantom dependencies and deprecated patterns.<\/strong> The architecture leans on a library or integration pattern that&#8217;s deprecated, sunset, or never existed.<\/li>\n\n\n\n<li><strong>Paradigm mixing without consistency.<\/strong> Event-driven async patterns combined with tightly coupled synchronous calls, creating failure modes nobody designed on purpose.<\/li>\n\n\n\n<li><strong>Context collapse in long design conversations.<\/strong> A recommendation late in a chat quietly contradicts a constraint the model agreed to twenty messages earlier.<\/li>\n<\/ul>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:50%\">\n<ul class=\"wp-block-list\"><div class=\"wp-block-image d-flex  justify-content-center m-3\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"\/wp-content\/themes\/restly-child\/assets\/images\/AI-architecture-decisions\/Common-hallucinations-in-AI-architecture-decisions.png\" alt=\"Common hallucinations in AI architecture decisions\"\/><\/figure>\n<\/div><\/ul>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-center atr-container is-layout-flow wp-block-column-is-layout-flow\">\n<h2 class=\"wp-block-heading pt-3 pb-3\">What Hallucinated AI Architecture Decisions Cost You Later<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The damage from a hallucinated AI architecture decision rarely shows up right away. That&#8217;s what makes it expensive.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Technical debt that compounds invisibly.<\/strong> Each AI architecture decision looks reasonable on its own. The problem is structural: choices optimised for the local problem, not the system as a whole. This flavour of AI technical debt tends to surface as an unbudgeted refactor, six to twelve months later.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Cross-system coupling nobody intended.<\/strong> A hallucinated integration pattern can quietly couple two services that should never have known about each other. By the time it&#8217;s visible, three more services depend on the same shortcut.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Compliance exposure with no audit trail.<\/strong> In regulated industries, a decision touching sensitive data needs a documented rationale. An AI architecture decision adopted without review leaves no record of who approved it. That&#8217;s a real gap under <a href=\"https:\/\/www.imt-soft.com\/ja\/2026\/04\/21\/what-is-enterprise-ai-types-risks-the-eu-ai-act\/\" style=\"color:#0d6efd;\" target=\"_blank\" rel=\"noopener noreferrer\"><u>EU AI Act<\/u><\/a> human oversight rules for high-risk systems.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Erosion of trust in either direction.<\/strong> Teams burned once by a hallucinated AI architecture decision tend to overcorrect. Some ignore AI input entirely, losing the speed gain. Others rubber-stamp it out of fatigue, which is worse. We cover this broader pattern in our article on <a href=\"https:\/\/www.imt-soft.com\/ja\/2026\/04\/29\/why-enterprise-ai-fails-in-production-security-data-governance-gaps\/\" style=\"color:#0d6efd;\" target=\"_blank\" rel=\"noopener noreferrer\"><u>why enterprise AI fails in production<\/u><\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<style>\n.atr-container{\nmargin-top: -20px !important;\nmargin-bottom: -25px !important;\n}\n\n.a-container{\nmargin-bottom:10px;\n}\n\n<\/style>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column has-background is-layout-flow wp-block-column-is-layout-flow\" style=\"background-color:#f7f7f7\">\n<div class=\"wp-block-columns container has-background is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\" style=\"background-color:#f7f7f7\">\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\">\n<h2 class=\"wp-block-heading pt-5 pb-3\">Architecture Validation: A Practical Framework for AI Architecture Decisions<\/h2>\n\n\n\n<div>\n<div class=\"info-box mt-4 mb-4\">\n  <h3>Quick answer:\n<\/h3>\n  <p>\nArchitecture validation for AI architecture decisions comes down to five checks: does it fit your real constraints, does it match existing patterns, how much does it touch if wrong, can it be undone, and is the rationale documented (architecture decision record)?\n <\/p>\n<\/div><\/div>\n<style>\n.info-box {\n\n border-left: 6px solid #2d4f8b !important; \n  background-color: #eef3fb;\n  padding: 15px;\n  font-family: \"Times New Roman\", serif;\n}\n\n.info-box h3 {\n  color: #2d4f8b;\n  font-size: 18px;\n  margin: 0 0 10px 0;\n}\n\n.info-box p {\n  color: #333;\n  font-size: 15px;\n  margin: 0;\n  line-height: 1.5;\n}\n<\/style>\n\n\n\n<p class=\"wp-block-paragraph\">Validating AI architecture decisions doesn&#8217;t require slowing every decision to a crawl. It requires asking the same five questions, every time.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Constraint check.<\/strong> Does this fit your real team size, stack, and traffic &#8211; not the generic case the model trained on?<\/li>\n\n\n\n<li><strong>Precedent check.<\/strong> Is this pattern already used elsewhere in your system, or does it quietly introduce a second paradigm?<\/li>\n\n\n\n<li><strong>Blast-radius check.<\/strong> If this AI architecture decision turns out wrong, how many services or data paths does it touch?<\/li>\n\n\n\n<li><strong>Reversibility check.<\/strong> Can this be undone cheaply next quarter, or is it a one-way door?<\/li>\n\n\n\n<li><strong>Documented rationale.<\/strong> What alternatives did the model consider, and why was this one chosen?<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">That fifth question is where architecture decision records earn their place. Architecture decision records (ADR) predate AI by years. They&#8217;re exactly the tool that closes the gap AI opens. An ADR captures the context, the alternatives, and the decision &#8211; in writing, before the code ships.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When the proposal comes from an AI tool, the ADR becomes something more. It&#8217;s where a human states, on the record, why they accepted or rejected it. That single habit turns an AI architecture decision into something that can be audited and reversed if it&#8217;s wrong.<\/p>\n\n\n\n<h2 class=\"wp-block-heading pt-4 pb-3\">Design Review Frameworks for AI Architecture Decisions<\/h2>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:50%\">\n<p class=\"wp-block-paragraph\">Validation answers whether one decision is sound. Design review frameworks answer who needs to look at AI architecture decisions, and when.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Tiered review by blast radius.<\/strong> Not every AI architecture decision needs a full review board. A naming convention can clear with a lightweight async check. A decision touching a shared data store or a customer-facing API needs full <strong>software architecture review<\/strong> before it merges. Scrutiny should scale with risk, not apply uniformly.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Require alternatives, not just answers.<\/strong> Prompt AI tools to surface the options they rejected, not only the final recommendation. A proposal with no visible alternatives is a red flag, whether it came from a model or a junior engineer.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Two-person sign-off on regulated paths.<\/strong> Any AI architecture decision touching GDPR-covered data or financial reporting should require sign-off from two people with system context. The cost of being wrong there is asymmetric.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>AI pre-screens, humans decide.<\/strong> AI tools can flag when a proposal conflicts with an existing architecture decision record. That pre-screening speeds up software architecture review without removing the human judgment call at the end. It&#8217;s the same principle behind AI code review across AI-augmented engineering: AI prepares the ground, a human makes the call.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:50%\">\n<ul class=\"wp-block-list\"><div class=\"wp-block-image d-flex  justify-content-center m-3\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"\/wp-content\/themes\/restly-child\/assets\/images\/AI-architecture-decisions\/Design-review-framework-for-AI-architecture-decisions.png\" alt=\"Design review framework for AI architecture decisions\"\/><\/figure>\n<\/div><\/ul>\n<\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading pt-4 pb-3\">Compliance-Safe AI Architecture Decisions in Regulated Industries<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">For leaders in financial services, healthcare, and enterprise software, validating AI architecture decisions isn&#8217;t only an engineering discipline. It&#8217;s a regulatory one.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/www.imt-soft.com\/ja\/2026\/04\/21\/what-is-enterprise-ai-types-risks-the-eu-ai-act\/\" style=\"color:#0d6efd;\" target=\"_blank\" rel=\"noopener noreferrer\"><u>EU AI Act<\/u><\/a> requires human oversight mechanisms for high-risk AI systems, and that obligation doesn&#8217;t stop at the code level. An architecture decision shaping how a high-risk system handles or logs data is part of what regulators expect documented.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.imt-soft.com\/ja\/2026\/04\/14\/eu-us-banking-compliance-in-2026-a-bfsi-guide\/\" style=\"color:#0d6efd;\" target=\"_blank\" rel=\"noopener noreferrer\"><u>DORA<\/u><\/a> adds another layer for financial institutions. ICT systems &#8211; and the architecture decisions behind them &#8211; need to be tested, documented, and recoverable from disruption. A new dependency or failure mode is exactly what DORA expects an institution to explain.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For enterprises across Germany, France, the Netherlands, and the broader EU, national supervisors are extending these expectations into day-to-day engineering governance. Architecture choices are increasingly something auditors ask about directly.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For Swiss institutions, FINMA&#8217;s guidance tracks closely with these EU standards. Swiss organisations serving EU clients are generally expected to meet the same bar. The same holds for US enterprises with EU exposure. A documented, reviewable trail behind every AI architecture decision is fast becoming the baseline.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The data infrastructure underneath this is its own discipline &#8211; how decisions get logged and made auditable. We cover it in our article on <a href=\"https:\/\/www.imt-soft.com\/ja\/2026\/05\/13\/ai-data-infrastructure-compliance-building-ai-ready-pipelines-in-2026\/\" style=\"color:#0d6efd;\" target=\"_blank\" rel=\"noopener noreferrer\"><u>AI data infrastructure and compliance<\/u><\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading pt-4 pb-3 container\">What Leaders Should Do About AI Architecture Decisions Right Now<\/h2>\n\n\n\n<p class=\"container wp-block-paragraph\">For CIOs, VPs of Engineering, and architects, here is the sequence for governing AI architecture decisions that holds up across regulated and unregulated environments alike.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"container\"><strong>Require an ADR for every AI architecture decision above a defined blast radius.<\/strong> Set the threshold now &#8211; by data sensitivity, system criticality, or both &#8211; rather than during an incident review.<\/li>\n\n\n\n<li class=\"container\"><strong>Build a tiered review gate into your design process.<\/strong> Not every decision needs the same scrutiny. Define what qualifies for lightweight review before anyone skips it under deadline pressure.<\/li>\n\n\n\n<li class=\"container\"><strong>Ask<\/strong><strong> AI tools for alternatives, every time.<\/strong> Make &#8220;what did you consider and reject&#8221; a standard prompt, not an occasional courtesy.<\/li>\n\n\n\n<li class=\"container\"><strong>Track AI technical debt like any other debt.<\/strong> If a decision ships with a known shortcut, log it, assign an owner, and revisit it on a schedule.<\/li>\n\n\n\n<li class=\"container\"><strong>Audit your architecture for AI-originated decisions nobody reviewed properly.<\/strong> Most teams have at least a handful. Finding them now is cheaper than finding them during an outage.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading pt-4 pb-3 container\">How IMT Solutions Strengthens AI Architecture Decisions<\/h2>\n\n\n\n<p class=\"container wp-block-paragraph\">IMT Solutions works with engineering organisations across financial services, healthcare, and enterprise software. We build architecture governance that holds up under AI-assisted velocity and regulatory scrutiny alike. Every AI architecture decision gets validated, documented, and reviewed at a level proportionate to its risk &#8211; not treated as a shortcut that bypasses judgment.<\/p>\n\n\n\n<p class=\"container wp-block-paragraph\">Building review infrastructure for AI-assisted design, or auditing decisions already in production? Explore our <a href=\"https:\/\/www.imt-soft.com\/ja\/case-studies\/\" style=\"color:#0d6efd;\" target=\"_blank\" rel=\"noopener noreferrer\"><u>case studies<\/u><\/a> or <a href=\"https:\/\/www.imt-soft.com\/ja\/contact\/\" style=\"color:#0d6efd;\" target=\"_blank\" rel=\"noopener noreferrer\"><u>contact our team<\/u><\/a> to talk through your environment.<\/p>\n\n\n\n<h2 class=\"wp-block-heading pt-4 container\">Frequently Asked Questions About AI Architecture Decisions<\/h2>\n\n\n\n<h3 class=\"wp-block-heading pt-3 pb-3 container\">What is a hallucinated AI architecture decision?<\/h3>\n\n\n\n<p class=\"container wp-block-paragraph\">A hallucinated AI architecture decision is a design recommendation that sounds plausible but doesn&#8217;t actually fit the system it was proposed for. It might be a pattern mismatched to team size, a fabricated performance guarantee, or an unfounded compliance claim. It&#8217;s dangerous because it&#8217;s indistinguishable in tone from a correct recommendation. The only reliable defence is a validation step that checks the decision against real constraints, not the model&#8217;s confidence.<\/p>\n\n\n\n<h3 class=\"wp-block-heading pt-3 pb-3 container\">How common are hallucinations in AI-generated code and architecture?<\/h3>\n\n\n\n<p class=\"container wp-block-paragraph\">Hallucinations are well documented at the code level. A USENIX-published study found that roughly one in five third-party packages recommended by sixteen code-generating models did not exist. Architecture-level hallucinations aren&#8217;t measured at that scale yet, but the cause is the same: models describe plausible patterns without verifying fit. The risk is arguably higher at the architecture level, where no compiler catches the error automatically.<\/p>\n\n\n\n<h3 class=\"wp-block-heading pt-3 pb-3 container\">What is an architecture decision record and why does it matter for AI?<\/h3>\n\n\n\n<p class=\"container wp-block-paragraph\">An architecture decision record (ADR) is a short document capturing the context behind a design choice, the alternatives considered, and the final reasoning. For AI architecture decisions specifically, the ADR is where a human records why they accepted or rejected a model&#8217;s recommendation. That turns a suggestion into a documented, auditable decision, not an unreviewed shortcut.<\/p>\n\n\n\n<h3 class=\"wp-block-heading pt-3 pb-3 container\">Does the EU AI Act apply to AI-generated architecture decisions?<\/h3>\n\n\n\n<p class=\"container wp-block-paragraph\">Yes, indirectly but materially. The EU AI Act requires human oversight mechanisms for high-risk AI systems. Architecture decisions that shape how such a system handles or logs data fall within what regulators expect documented. An AI architecture decision adopted without review or rationale is a gap in exactly the oversight the Act requires.<\/p>\n\n\n\n<h3 class=\"wp-block-heading pt-3 pb-3 container\">How should teams design review gates for AI-generated architecture proposals?<\/h3>\n\n\n\n<p class=\"container wp-block-paragraph\">Review gates should scale with blast radius, not apply uniformly. Low-impact AI architecture decisions &#8211; naming conventions, internal-only endpoints &#8211; can clear with a lightweight async review. Decisions touching shared data stores or regulated workloads need a full design review, plus sign-off from two people in regulated settings. The goal is proportionate scrutiny, not scrutiny on every decision.<\/p>","protected":false},"excerpt":{"rendered":"<p>AI Architecture Decisions: How to Catch Hallucinations Before They Ship AI shouldn&#8217;t decide your architecture alone. That&#8217;s not a warning for junior developers experimenting with a chatbot. It&#8217;s a structural fact about AI-augmented engineering. AI tools are excellent at implementing known patterns. They are unreliable at choosing between them. Architecture is nothing but choosing. Every AI architecture decision is a judgment call about tradeoffs &#8211; a queue here, a data store there, a trust boundary somewhere else. A model can describe that judgment call fluently. It has no real grasp of what it costs your system six months later. You&#8217;re past the &#8220;AI writes my boilerplate&#8221; conversation. The harder question: [&hellip;]<\/p>","protected":false},"author":7,"featured_media":7298,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_mi_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[331,9],"tags":[462,507,506,508,509],"class_list":["post-7297","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-latest","tag-ai-code-review","tag-ai-technical-debt","tag-architecture-decision","tag-architecture-review","tag-software-architecture"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>AI Architecture Decisions: Catching Hallucinations - IMT Solutions<\/title>\n<meta name=\"description\" content=\"AI architecture decisions can sound confident and still be wrong. 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