AI tools have made individual contributors faster.
They have not made enterprise delivery safer.
In a recent modernization program, we used AI for:
- Requirement extraction from legacy code
- Service decomposition suggestions
- Test case generation
All worked — locally.
What failed was global consistency.
Example:
An architectural decision explicitly externalized eligibility logic for regulatory change.
That constraint existed in design documents.
It was not enforceable during AI-assisted development.
Six sprints later:
- Generated services re-embedded the logic
- Tests validated behaviour, not architectural intent
- Audit required explanation that no longer existed
The issue wasn’t AI accuracy.
It was absence of design-time authority at runtime.
What corrected this was enforcing a system-level rule:
- Architecture decisions became executable guardrails
- AI agents operated inside those constraints
- QE derived scenarios from the same decision lineage
At SDLC scale, intelligence without authority increases risk.
Deterministic execution under architect-defined guardrails reduces it.
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