Implementing the AWS AI-DLC Standard
Stop reading your roadmap
through a straw.
The AI-DLC Workspace installs the technical infrastructure your AI tools were always missing — Stateful MCP Servers that enforce phase gates, Steering Docs that preserve intent, and Git Hooks that block bad merges. Plus a Visual Orchestrator that visualizes every Unit of Work and Bolt across your organization.
Why AI Adoption Stalls
The tools got faster. The infrastructure stayed the same.
The AI-Assisted Trap
Developers use AI to autocomplete code inside the same workflow they already had. The result: faster silos. Each engineer gets a personal speed boost, but the team never changes how it plans, reviews, or ships. You get faster typing, not faster delivery.
The AI-Autonomous Trap
Teams point an agent at a ticket and let it run. The result: it builds the wrong thing — confidently. Without structured requirements, approval gates, and human checkpoints, autonomous AI creates more rework than it saves.
The AI-DLC Standard is the third path. A technical infrastructure where Stateful MCPs enforce phase gates, Steering Docs preserve intent, and Git Hooks ensure human approval at every step. Not assisted. Not autonomous. Infrastructure by design.
The Engine (What You License)
Stateful MCPs. Steering Docs. Git Hooks. The infrastructure you install.
Stateful MCP Servers
The software your team runs. Maintains Persistent Context across Inception, Construction, and Operations. Phase gates enforced automatically. This is infrastructure you install — not a methodology you practice.
Steering Docs
Org standards, team conventions, system architecture — captured as structured documents that MCPs load automatically. Every AI interaction starts with the right context, not a blank slate. Intent is preserved from Inception through Operations.
Git Hooks
The enforcement layer. Deterministic validation — block non-compliant merges, validate artifact structure, enforce phase progression. Run on every push. Installed into your repos. Licensed per seat.
Visual Orchestrator
The bird's-eye view for TPMs and Engineering Leads. Visualizes every Unit of Work and Bolt across squads — status, phase, ownership, blockers. Synchronized with the IDE via the Git Source of Truth. Stop managing delivery through a flat folder tree.
The Lifecycle
Three phases. Every role. AI-native from first requirement to production.
Phase 1
Inception
Hours to days
Turn a business goal into structured requirements, a design document, and decomposed implementation units — all AI-generated, all human-approved. The sprint starts with shared clarity, not a vague ticket.
Phase 2
Construction
Days to weeks
AI picks up each unit, proposes an implementation plan, asks clarifying questions, then writes the code. Engineers review every plan and every output. Nothing merges without human approval.
Phase 3
Operations
Ongoing
AI generates deployment plans, runbooks, and monitoring configurations. The team reviews, approves, and ships. Post-deployment, the Workspace tracks incidents and feeds lessons back into the knowledge layer.
How It Works
The same pattern. Every phase. AI proposes. Humans decide.
AI proposes a plan
Based on structured context and requirements, the AI generates a concrete, reviewable plan for the next piece of work.
AI asks clarifying questions
If the AI detects ambiguity, missing context, or conflicting requirements, it pauses and asks — instead of guessing.
Human approves
A human reviews the plan, answers questions, and gives explicit approval before any execution begins.
AI executes. Human validates.
The AI carries out the approved plan. The human reviews the output, requests changes if needed, and signs off.
This loop repeats at every phase — Inception, Construction, and Operations. The result: every AI-generated artifact has a clear audit trail, a human decision behind it, and a structured path to production.
See It in Motion
The Multi-Markdown Context Nightmare — and the fix.
The problem it solves: As a project grows, your AI context lives in dozens of .md files scattered across a flat IDE tree. requirements.md, design.md, unit-3-context.md, deployment-plan.md — nobody can see how they connect. Nobody can see what's approved, what's in-review, what's blocked.
The Visual Orchestrator replaces the flat folder tree with a high-fidelity visual graph of your project state. Every Unit of Work. Every Bolt. Every dependency. Status, phase, ownership — rendered in a single view, synchronized to your git history in real time.
It's the management layer your Engineering Lead has been missing.
BUG-441 · Broken Discount Code
Phase: Inception · Requirements
Acceptance criteria: Discount code SAVE20 applies correctly at checkout. Edge cases defined. Tests specified.
Waiting for approval...
Waiting for context...
Source of Truth
Git is the Source of Truth. Always.
Every requirement, approval, and decision the Workspace creates is a Git PR — not a record in our database. The Workspace reads and writes to your Git PRs.
“The approval is the merge. The audit trail is the git log.”
Deployment Options
Hosted or self-hosted. Running this week either way.
Workspace + Orchestrator
$50 / seat / mo
The full platform — Stateful MCPs, Git Hooks, Visual Orchestrator, and delivery metrics. Connect your repos and start running the AI-DLC Standard today.
Workspace + Implementation
Custom pricing
Everything in Workspace + Orchestrator plus 90-day Expert Installation, SSO/SAML, any git provider, and VPC deployment.
Workshop + Workspace
$15K – $25K
2-day hands-on engagement. We install the Workspace on your repos, run the AI-DLC Standard on real work, deliver practitioner certification for all participants, and provide an implementation roadmap. Workspace licenses included.
What Running It Looks Like
When the AI-DLC Workspace is installed, engineering stops being the bottleneck.
“We went from two-week sprints with half the tickets done to shipping every planned unit in a week. The process didn’t slow us down — it removed the ambiguity that was slowing us down.”
60%
reduction in cycle time
3x
more units shipped per sprint
< 5%
rework rate on AI-generated code
Is This Right for Your Team?
Three types of teams the AI-DLC Workspace isn't for.
Teams not yet using AI in development.
If your team hasn't started experimenting with AI coding tools, you need adoption before you need a lifecycle. We're happy to chat, but the Workspace isn't your first step.
Solo developers.
AI-DLC shines on teams where coordination is the bottleneck. If you're a solo dev, the overhead of structured phases and approval gates will slow you down, not speed you up.
Teams still deciding whether AI belongs in their workflow.
The Workspace assumes the decision is made. If you're still evaluating whether AI-assisted development is right for your org, start with a pilot — not a process overhaul.
If none of those fit — let’s talk.
The SimplyGoose Difference
Life before vs. life after the AI-DLC Workspace.
| Category | Before SimplyGoose | After SimplyGoose |
|---|---|---|
| Delivery Velocity | 10x Individual Output, 1x Team Throughput Individual engineers are 10x faster, but the team still delivers at the same pace due to manual handoffs and reviews. | 10–15x Team Throughput The engineering team moves in high-velocity Bolts, with AI-ready requirements and automated construction phases. |
| Project Visibility | The "AI Black Hole" Leadership pays for AI licenses but has zero visibility into cycle times, phase distribution, or cross-squad blockers. | Full Audit Trail Every decision — from Inception to Deployment — is logged in the Visual Orchestrator and permanently stamped in the Git history. |
| Context Management | The "Context Forest" Managing 20+ .md requirement files in a flat IDE folder tree creates massive cognitive load for TPMs and Leads. | The Visual Graph The Orchestrator manages the hierarchy of Units of Work, while the Stateful MCP injects the correct context directly into the IDE. |
| Governance & Risk | Rogue AI & Hallucinations AI builds the wrong thing because of ambiguous input, or pushes non-compliant code that humans must fix manually. | Deterministic AI Git Hooks block non-compliant code at the gate. AI executes exactly what the human approved during Mob Elaboration. |
| Mental Model | Legacy SDLC Rituals Forcing AI into 2-week "Sprints" designed for human-speed planning. | The AI-DLC Standard Shifting to Inception, Construction, and Operations phases, where AI handles the routine and humans make the decisions. |
| Source of Truth | Siloed Slack/Jira Threads Critical logic is scattered across Jira tickets and chat, disconnected from the actual code. | Git-Native Truth Requirements live in the repo. The AI-DLC Workspace reads and writes to your PRs, ensuring the context never rots. |
Before
10x Individual Output, 1x Team Throughput
Individual engineers are 10x faster, but the team still delivers at the same pace due to manual handoffs and reviews.
After
10–15x Team Throughput
The engineering team moves in high-velocity Bolts, with AI-ready requirements and automated construction phases.
Before
The "AI Black Hole"
Leadership pays for AI licenses but has zero visibility into cycle times, phase distribution, or cross-squad blockers.
After
Full Audit Trail
Every decision — from Inception to Deployment — is logged in the Visual Orchestrator and permanently stamped in the Git history.
Before
The "Context Forest"
Managing 20+ .md requirement files in a flat IDE folder tree creates massive cognitive load for TPMs and Leads.
After
The Visual Graph
The Orchestrator manages the hierarchy of Units of Work, while the Stateful MCP injects the correct context directly into the IDE.
Before
Rogue AI & Hallucinations
AI builds the wrong thing because of ambiguous input, or pushes non-compliant code that humans must fix manually.
After
Deterministic AI
Git Hooks block non-compliant code at the gate. AI executes exactly what the human approved during Mob Elaboration.
Before
Legacy SDLC Rituals
Forcing AI into 2-week "Sprints" designed for human-speed planning.
After
The AI-DLC Standard
Shifting to Inception, Construction, and Operations phases, where AI handles the routine and humans make the decisions.
Before
Siloed Slack/Jira Threads
Critical logic is scattered across Jira tickets and chat, disconnected from the actual code.
After
Git-Native Truth
Requirements live in the repo. The AI-DLC Workspace reads and writes to your PRs, ensuring the context never rots.
SimplyGoose is not another database silo. We operate on top of your existing Git repository, ensuring you own your context forever.
Install the AI-DLC Workspace. Start shipping this week.
Stateful MCPs, Steering Docs, Git Hooks, and the Visual Orchestrator — installed into your repos in minutes. See what structured AI delivery looks like on your actual codebase.