What stages should an AI-assisted feature pass through before release?

TL;DR

Move from intake to specification, planning, implementation, review, checks, documentation, and closure, preserving the decision and evidence at each boundary. Start with one real change and make the evidence visible beside the work. Check accepted need, reviewable plan, verified change, closure record. If a reviewer still has to guess about scope, proof, or ownership, pause and repair that gap before scaling the practice. The result should be a workflow another qualified person can follow and challenge without relying on private chat or the original operator’s memory.

In this article

1. Intake and planning expose the real surface
2. Specification gives reviewers an independent target
3. Development, review, and checks separate responsibilities
4. Documentation and delivery preserve the result
5. Use a four-part evidence check
6. Frequently Asked Questions

Last updated October 14, 2026

6 min read

Move from intake to specification, planning, implementation, review, checks, documentation, and closure, preserving the decision and evidence at each boundary. The useful question is not whether the team can produce an impressive output once. It is whether the work remains understandable, reviewable, and safe when another person follows it under normal delivery pressure.

Intake and planning expose the real surface

Intake identifies the request, requester, affected product behavior, and likely risk. Planning then maps modules, owners, reuse options, dependencies, rollback, and unresolved decisions. For the refund story, that means payment state and accounting are visible before an agent edits a controller.

NIST AI Risk Management Framework helps bound this point. This is voluntary risk guidance, not certification or proof of a product outcome.

Lens	What to inspect
1	Stage 1-2: scope
2	Stage 3: intent
3	Stage 4-6: change and evidence
4	Stage 7-8: shared memory and receipt

The numbers and labels above are a diagnostic, not benchmark data. Replace them with repository evidence before using the model in a staffing or investment decision.

Specification gives reviewers an independent target

Specification records acceptance criteria, invariants, failure behavior, and proof methods. It should be versioned for the current attempt. Without that baseline, a reviewer compares code with memory. With it, the team can ask whether the refund amount, authorization rule, duplicate request behavior, and event trail match the agreed target.

1. Route the request. Record the artifact, owner, and evidence needed before the next transition.
2. Baseline the specification. Record the artifact, owner, and evidence needed before the next transition.
3. Build and challenge the change. Record the artifact, owner, and evidence needed before the next transition.
4. Sync documentation and issue a receipt. Record the artifact, owner, and evidence needed before the next transition.

The sequence matters because later evidence depends on earlier intent. Skipping one step transfers uncertainty to a reviewer who has less time and often less context.

Development, review, and checks separate responsibilities

Development performs bounded edits. Review challenges design and implementation. Checks execute what can be tested deterministically. Keeping the worker and final reviewer separate reduces self-confirmation, but no separation creates a correctness guarantee. Evidence stays bounded by test and oracle quality.

Google DORA 2025 adds a second boundary. The report is observational, so associations should not be presented as universal causation.

Mistake: Collapsing eight different questions into one long prompt. Fix: Let each stage produce the smallest artifact needed for the next accountable decision.

The correction is deliberately procedural. A workflow can be inspected, rehearsed, and improved. A warning without an owner or artifact rarely survives the next busy sprint.

Documentation and delivery preserve the result

Documentation sync updates every affected surface, then delivery records status, decisions, checks, and known blind spots. In paqad-ai v1.67.0, the public framework describes risk lanes, specialist roles, automatic checks, and local audit records. Treat these as product mechanisms to inspect, not independent outcome proof.

paqad-ai v1.67.0 was the current public release when this article was verified on July 21, 2026. Its public repository describes local workflows, risk routing, specialist roles, deterministic checks, documentation sync, and audit records. Those are product mechanisms, not independent proof that a team will achieve a specific outcome.

Use the broader AI workflow audit guide to map the operating system, compare the evidence bar with production-ready AI code, and use the consultant selection guide when outside ownership is being considered.

• Request and owner recorded
• Modules and risk routed
• Acceptance criteria baselined
• Checks mapped to behavior
• Docs and delivery receipt complete

The decision rule for this article is: Use the lightest lane that preserves the evidence and human authority required by the feature’s risk.

The handoff is complete only when the next role can act from the stored artifact without asking the previous role to reconstruct intent.

Use a four-part evidence check

The practical test is whether another qualified person can understand the work without the original operator narrating it. For what stages should an ai-assisted feature pass through before release?, inspect four things together. A polished output with a missing decision owner is still weak. A detailed policy with no repository evidence is equally weak.

Review surface	Question to ask	If the answer is unclear
Accepted need	Can the team point to it without reconstructing the story from chat?	Write it down beside the work.
Reviewable plan	Is the boundary explicit enough that two reviewers would reach the same starting point?	Narrow the scope before continuing.
Verified change	Does the evidence show what ran, what changed, and what remains unknown?	Retain the result as a review artifact.
Closure record	Is one person accountable for accepting, changing, or stopping the work?	Name the owner before the next handoff.

This worksheet is intentionally small enough to use on a real change. Start with one recent item that felt slow, risky, or surprisingly hard to review. Follow it from the original request to its present state. Record where a reviewer had to guess, where evidence disappeared, and which decision arrived late. The gaps show whether the next improvement belongs in documentation, automation, planning, review capacity, or ownership.

For example, imagine that the final code looks plausible and the tests are green, but nobody can connect the change to an accepted requirement. More testing will not repair that missing intent. The team must recover the requirement, decide whether the implementation still fits it, and preserve that link for the next reviewer. The same logic applies when the request is clear but the executed checks, risk boundary, or approval are missing.

Use one stop rule: if the person responsible for the outcome cannot explain what evidence supports the next step, pause before widening the change. Move from intake to specification, planning, implementation, review, checks, documentation, and closure, preserving the decision and evidence at each boundary. This keeps speed connected to a decision the team can defend.

If this problem is affecting live delivery, Eliyce can help you turn the diagnosis into a bounded Paqad framework change with explicit evidence and decision points. See the relevant Eliyce service.

Frequently Asked Questions