Speed is no longer the scarce asset. Controlled speed is.
Agents can produce the work. SDF does not replace your AI tools; it gives AI-assisted delivery discipline before the PR exists. Scope, evidence, verification, risk, and handoff travel with the change so engineers, reviewers, tech leads, and engineering leaders can understand, review, and own the work after the agent is done. Start with a readiness check, install it where the repo fits, and run one governed change before your team decides what ships.
The gap
Unmanaged AI-assisted delivery can turn speed into unclear intent, unreviewable PRs, fragile changes, weak verification, security or data risk, wasted agent loops, and rework that compounds faster than the team can inspect it.
The bottleneck moves from writing code to deciding what should be done, reviewing what changed, proving what was checked, and handing over work the team can still maintain. Review confidence becomes the constraint.
When something goes wrong, the leadership question is not which model wrote the code. It is what was asked, what changed, what was checked, who reviewed it, and why the work was safe enough to trust.
SDF is not more process and does not force every engineer into the same AI tool, agent loop, IDE, or personal workflow. It packages the delivery habits serious teams already care about - intent, acceptance criteria, scope boundaries, risk notes, verification evidence, review context, and handover - into a shared quality bar at the PR/MR boundary.
Prompts, scope, evidence, and review boundaries become inspectable instead of disappearing into chat history.
AI-assisted PR volume can grow faster than review habits. SDF gives teams a governed path to attach usage signals and review evidence where available.
Human approval remains the gate. Evidence supports review, not automatic approval, merge, deploy, or repo mutation.
Playbooks, acceptance criteria, verification evidence, and risk/confidence limits make the quality bar explicit.
Agents can add packages before ownership, upgrade burden, or hidden-domain impact is reviewed. SDF makes dependency changes reviewable decisions before they land.
The governed record shows what was asked, what changed, what was checked, and where risk remains instead of leaving leaders accountable for undocumented judgement.
Before / after
AI can increase the volume of pull requests. SDF helps teams keep review confidence by attaching the evidence reviewers need before they are asked to trust the change.
Code review asks whether the code looks okay. Governed delivery starts before the PR exists: whether the change is acceptable to merge, and what future humans or agents should not have to rediscover.
Before SDF
Standard AI PR
Reviewer gets a useful summary, but still has to infer the delivery evidence.
Testing notes
Checks may be listed, but risk, standards, and merge confidence still need to be reconstructed.
After SDF
Review focus
Reviewer starts with the shape of the change, not just the diff.
Run context
The original request and run context travel with the PR.
Standards applied
Reviewers see which standards shaped the work before they review the code.
Verification evidence
The PR carries evidence of alignment, verification, and boundaries.
Product journey
SDF standardises the quality bar, not the developer workflow.
Start with repo suitability, install the Front Door where it fits, run one bounded governed change, and let your team decide whether the workflow should repeat.
The output is a governed PR with evidence attached: intent, acceptance criteria, scope, risk, verification, evidence, and handover context before reviewers are asked to trust the change.
Every evidence artifact should have a job: help the current work, help reviewers, preserve context, reduce rework, or improve the next run. That is how governed changes keep the handoff clear instead of making every reviewer start cold.
SDF checks whether your repo, review path, and verification surface are a fit for the Front Door approach.
SDF adds the review boundary and evidence workflow without forcing every developer into the same coding tool.
AI-assisted or developer work is captured with intent, acceptance criteria, scope, risk, verification, evidence, and reusable handover context.
Reviewers request changes, merge, reject, or repeat the workflow. Human review, merge, deploy, and production control stay with your team.
What you get
You get a suitability readout, a Front Door path where the repo fits, one governed change, evidence and verification context, and a customer-controlled decision about what ships.
SDF does not automatically approve, merge, deploy, mutate your repo, or take production action.
Visible blockers, hidden-risk areas, and whether a Front Door install plus one governed change makes sense.
Where useful, a check-only review boundary with evidence mapping, risk/limit notes, and human/operator review.
One bounded governed PR with verification context, work item evidence, and reusable reviewer handover attached.
Your team approves scope, reviews the PR, controls merge and deploy, and decides what should become repeatable.
Ready to see whether your repo is a fit?
Start your readiness checkWho it is for
Get a credible path from AI coding experimentation to accountable delivery.
Find uneven practices, unclear review expectations, and governance gaps before they scale.
Evaluate whether AI-assisted work arrives with enough intent, evidence, verification, risk context, and ownership to review responsibly.
Power users can keep advanced workflows while sceptical engineers get a clearer PR/MR surface before being asked to trust the output.
Workflow proof
Agents can produce the work. SDF makes the work reviewable.
A governed AI-assisted PR is the proof surface for what was asked, what changed, what was checked, what risk remains, and what the reviewer needs before a human merge decision.
SDF evidence is not throwaway PR paperwork. Every governed change leaves behind a bounded record future humans and agents can use instead of starting cold. The current capability is reviewer-facing evidence, handoff context, and run context where available. SDF starts in check-only, review-led mode; it does not automatically approve or merge.
Why SDF works
SDF is on the side of engineers: the people asking agents to do the work, reviewing the work, and carrying responsibility for what ships.
The operating model is tested before it reaches a customer repo. It is dogfooded across real apps, local and cloud agents, providers, models, reasoning settings, and workflow failure modes so customers do not have to run every experiment themselves.
The point is controlled speed, not governance theatre. SDF adds the minimum useful control needed to keep agentic delivery fast, reviewable, testable, sustainable, and safe for a team to absorb.
This is credibility support, not a claim of automatic approval, universal repo support, hosted enforcement, measured savings, or production governance.
Visit the Research LabThe method keeps the delivery habits serious teams already rely on when AI increases output volume.
SDF learns from governed work across apps, agent surfaces, model choices, reasoning settings, and failure modes before customer use.
Every artifact should help the work, the reviewer, the handoff, future context, or the next run. If it does none of those jobs, it should be challenged.
Evaluation questions
Based on real evaluation conversations with teams looking at governed AI-assisted delivery, these are the practical questions that usually come up before a first assessment or PoC.
No. SDF is not another AI coding tool. Teams can keep using Codex, Claude Code, Cursor, Copilot, or whatever comes next. SDF is the governed delivery layer around those tools: scope before execution, evidence during delivery, and handoff after the PR.
No. SDF does not replace engineering judgement. It protects it. Reviewers still decide what is acceptable; SDF gives them the context, evidence, verification, risk, ownership, and handoff they need before they are asked to trust AI-assisted work.
Yes. Strong teams can assemble parts of this with AI coding tools, CI, PR templates, and internal standards. SDF productizes the shared quality bar so power users can keep advanced workflows while reviewers get a consistent PR surface for intent, evidence, verification, risk, and ownership.
No. Code and verification matter, but many delivery risks hide outside the diff: product rules, ownership boundaries, provider coupling, permissions, persistence, and approval authority. SDF helps make those domains explicit for human review before work is approved.
The intended model is that source code, branches, PRs, specs, prompts, run logs, test output, and review evidence can remain inside your approved environment. The current workflow is assisted and scoped before deeper integration.
Choose one bounded work item, journey, or workflow. Run the same spec and acceptance criteria through your current workflow and an SDF governed workflow, then compare review evidence, rework, test visibility, handover quality, and cost visibility.
Governance must earn its keep. Evidence should help the current run, help the reviewer, preserve useful context, improve handoff, reduce rework, or help the next human or agent. If it does none of those jobs, it should be challenged or removed.
Model spend is visible, but review burden, failed work, rework, audit preparation, and unmanaged risk are often hidden. SDF does not claim measured savings. It captures AI usage signals where available so teams can review whether work used the right context, model, scope, and reasoning effort.
It is not a self-serve hosted scan, automatic repair, hosted enforcement, security certification, or automatic approval, merge, deploy, or repo mutation. Start with readiness, then prove the approach on a bounded governed PR where it fits.
Have a different evaluation question? Start with a readiness check and we will agree the safest next step manually.
Start your readiness checkNext step
Tell us what you are trying to scale with AI-assisted delivery. We will review lightweight readiness signals first, then follow up manually if a Front Door install and first governed change path makes sense.
Human-reviewed, customer-controlled, and scoped before any install. No automatic approval, merge, deploy, hosted enforcement, or repo mutation.
