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When AI Architecture Suggestions Are Dangerous

5 min read
Software ArchSolutions ArchEnterprise Arch

Solutions Arch

Client-specific constraints are invisible to AI. Double-check every suggestion.

Enterprise Arch

AI loves 'best practice.' Your org may need 'least change' instead.

When AI Architecture Suggestions Are Dangerous

TL;DR

  • AI suggestions often look textbook-perfect. Real systems rarely are.
  • The danger is confidence: AI sounds sure. Your context might make its answer wrong.
  • Red flags: over-engineering, ignoring constraints, "one size fits all" patterns.

AI can produce architecture that reads like it came from a senior staff engineer. Diagrams, trade-offs, even ADRs. The problem isn't that AI is stupid — it's that it's confident. It doesn't say "I don't know your scale" or "this might not fit your team." It just gives you the answer.

Classic AI Architecture Traps

1. Over-engineering for your scale

  • AI suggests microservices, event sourcing, CQRS. You have 3 engineers and 10K users.
  • The right answer might be a monolith with clear boundaries. AI won't volunteer that.

2. Ignoring org constraints

  • "Add a dedicated team for this service" — you don't have headcount.
  • "Use Kafka" — your team has zero streaming experience.
  • AI doesn't know your org chart or skill matrix.

3. Vendor and stack lock-in

  • AI often reflects popular blogs and docs. That means AWS-heavy, or Kubernetes-everything.
  • Your company might be on GCP. Or on-prem. AI won't ask.

4. Copy-pasted "best practices"

  • AI loves idempotency, retries, circuit breakers. All good — until they're applied without considering your failure domain.
  • Sometimes the "dumb" solution is fine. AI rarely suggests it.

5. Invisible coupling and hidden complexity

  • AI can produce a clean diagram. Operational reality: 47 new things to monitor, deploy, and debug.
  • "Just add a message queue" sounds simple. Running it is not.

How to Spot the Trap

Ask yourself:

  • Would I have suggested this without AI? If not, why does AI think it's right?
  • Does this require skills/resources we don't have?
  • Are we solving today's problem or a hypothetical future one?
  • What's the simplest thing that could possibly work? Did AI suggest that?

The Verification Habit

Before adopting any AI-suggested architecture:

  1. Stress-test the assumptions — "This assumes we have X. Do we?"
  2. Run it by someone who's been burned — Find the person who ran Kafka in prod and ask what they'd do differently.
  3. Start smaller — Can you pilot one piece before committing to the whole design?

Manual process. Repetitive tasks. Limited scale.

Click "With AI" to see the difference →

Quick Check

What remains human when AI automates more of this role?

Do This Next

  1. Recall one AI-suggested design you've seen (yours or a colleague's). List 3 assumptions AI made that might not hold in your context.
  2. Create a "AI architecture review" checklist — Org fit, skill fit, scale fit, timeline fit. Run every AI-assisted design through it.
  3. When in doubt, bias toward simplicity — If AI suggests the complex option, ask: "What's the minimal version?" You might not get a good answer. That's information.