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Respect each specialty
No one contributor is expected to replace every specialist. The people responsible for product, domain, security, reliability, architecture, quality, or delivery judgment contribute it where the change needs it.
The Workflow
Specification-First Delivery does not ask one engineer, or one AI system, to replace the knowledge held across product, domain, architecture, quality, security, and operations. It makes that judgment available, reviewable, and usable throughout delivery.
The working agreement
The workflow gives each contributor a clear role in producing an organizational result. It does not collapse product, engineering, domain, quality, security, and operational accountability into one generalized role.
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No one contributor is expected to replace every specialist. The people responsible for product, domain, security, reliability, architecture, quality, or delivery judgment contribute it where the change needs it.
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Every material document has an accountable owner. AI can draft and challenge it, but it does not own the document or substitute for its review.
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A confirmed answer improves the Knowledge System rather than remaining in a private conversation or in one person’s memory.
The question and review cycle
This cycle recurs while discussions, specifications, architecture, technical work, and test specifications are developed. When AI has no further material questions, that is a review signal, not a transfer of human responsibility.
Use AI to inspect the request, existing code, documented decisions, and relevant constraints for missing information, conflicts, exceptions, and dependencies.
When a question falls within another specialty, ask that colleague to decide, clarify, or review it rather than inferring an answer.
Record the decision in the appropriate discussion, specification, glossary, methodology, constraint, or decision-history source.
The named owner examines the resulting document for accuracy, clarity, scope, constraints, protected behavior, and acceptance conditions.
The delivery flow
The stages below show how a request moves from a considered discussion into clearly scoped implementation. The question and review cycle may return the work to an earlier stage whenever the evidence requires it.
Discuss the new feature, application, or requirement before deciding how to implement it. Make the current product intent, decision history, code, constraints, interfaces, and operating context available to the people and AI systems examining the change.
Ask AI to identify open questions, answer them with the responsible colleagues, then revise the document. Repeat until AI identifies no further material questions from the available context, and the owner judges the discussion ready to specify.
Describe the behavior people must be able to observe, including normal paths, exceptions, errors, dialogs, business rules, and acceptance conditions. Non-functional specifications state reliability, observability, alerting, security, performance, or technical requirements when those are necessary.
Where a change introduces or changes system responsibilities, interfaces, dependencies, technology choices, or significant constraints, translate the relevant specifications into an architectural design. Patches and low-risk changes may not require this stage.
Translate the agreed requirements and any architectural design into technical work that engineers or AI coding agents can execute. A larger requirement may legitimately split into three, twenty, or another appropriate number of technical specifications.
Once engineers judge a technical specification ready, the execution instruction can be as direct as “Implement the specification.” New information does not get silently absorbed into code. It returns to the accountable owner and the appropriate earlier artifact.
A parallel quality path
QA and test engineers develop test specifications from the functional and non-functional specifications. This keeps acceptance evidence connected to the outcome and operating expectations that were agreed before code was written.
Translate the specified behavior, exceptions, reliability expectations, observability requirements, and alerting conditions into testable scenarios and evidence.
QA engineers ask AI to surface unanswered test questions, update the Knowledge System, and review the test specification line by line. Questions that belong to another specialty return to that accountable colleague.
Tests and operational evidence show whether the agreed conditions were met. They support acceptance decisions without redefining the requirement after implementation.
Capability Continuity
The workflow continuously improves the shared system of knowledge and evidence. It reduces reliance on one person’s memory without reducing the value of professional judgment.
Everyone brings a different skill set. Specification-First Delivery helps those people turn their judgment into a shared delivery capability, so AI strengthens organizational results instead of making people fear replacement.
Begin with one delivery stream