AI supports option generation, documentation, interface analysis and test design. Architectural quality still comes from explicit requirements, reviewable decisions and real system behaviour.
What the concept actually means
Architecture prompts need context: business goals, quality attributes, existing estate, data, interfaces, operational constraints and decision status. Without them the model optimises for generic patterns.
Why it matters in the enterprise
AI suggestions are treated as hypotheses. Trade-offs are recorded in architecture decision records; tests verify contracts, failure behaviour, security and operability.
A controlled method
The CTPM practice framework for controllable AI applications uses seven stages: understand the task, clarify context and data, apply AI deliberately, review professionally, handle deviations, approve accountably and document transfer. It is a transparent working framework, not a certification.
- Define task and impact
- Clarify data, context and permissions
- Review against domain criteria
- Control deviations, approval and evidence
CTPM practice example
CTPM practice example: AI proposes three integration options for a document interface. The team evaluates latency, data ownership, failure coupling, restartability and operations, then records the decision with test assumptions.
Quality and test criteria
The following criteria make quality observable for this use case:
- Decisions reference concrete quality attributes.
- Interface contracts include error and version rules.
- Tests cover normal, load and failure paths.
- Generated code follows the same review as human code.
Risks and common misconceptions
Risks include fabricated APIs, obsolete library patterns, vulnerabilities, missing operational concerns and architecture prose without a reviewable decision.
Example transfer artefact
Transfer artefact: an architecture and integration sketch with ADR, interface contract, test strategy, quality criteria and open risks.