Implementing the AWS AI-DLC Standard
The Operating
System for
AI-Driven
Teams.
Individual AI tools make contributors faster. SimplyGoose installs AI-DLC — the Stateful MCPs, Steering Docs, and Git Hooks needed to turn AI speed into enterprise delivery.
Stateful MCPs. Git Hooks. Visual Orchestrator. All in git.
The Gap
AI tools are running everywhere. Nothing is connected.
AI tools made individual contributors faster. They didn't make the process faster.
Your team already has AI agents. Copilot in the IDE. Claude in the terminal. AI-assisted tickets in Jira. Each one is making that role faster in isolation.
But there's no shared thread. Product writes a ticket — the engineer's agent never sees the full intent behind it. The engineer ships a PR — the Engineering Lead doesn't know what decision was made or why. AI is running at every step, but each step is its own island.
The problem isn't that AI is missing. It's that the lifecycle has no connective tissue. The AI-DLC Workspace is that connective tissue — Stateful MCPs, Steering Docs, and Git Hooks that wire your tools into a single delivery system.
Why Existing Approaches Don't Fix It
Two failure modes. Neither one solves it.
What most teams are doing
Faster silos. Same fragmented process.
AI writes the function. AI fixes the bug. Each task is faster in isolation. But nothing connects. Context evaporates between roles. The process stays fragmented. Faster fragments, same incoherent outcome.
What vendors keep promising
Builds fast. Builds the wrong thing.
AI decides. AI plans. AI builds. The demo looks impressive. In production: technically correct, strategically wrong. Accountability is murky. Nobody can explain why a decision was made. Hard to audit. Harder to fix.
There's a third path.
AI executes. Humans decide. At every step, by design. With a structure that makes the whole team faster — not just individual contributors.
That's the AI-DLC Standard. And the AI-DLC Workspace is how your team runs it.
The AI-DLC Impact
Stop fighting the friction. Start running the process.
| 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.
AI-DLC Workspace
The infrastructure your AI tools
were always missing.
The AI-DLC Workspace installs Stateful MCP Servers, Steering Docs, and Git Hooks directly into your repositories — the technical infrastructure that turns individual AI velocity into coherent, measurable team delivery.
It's not a project management tool. It doesn't replace Claude Code, Kiro, or Copilot — it's the infrastructure layer that makes those tools work at team scale. Plus a Visual Orchestrator that gives TPMs the bird's-eye view they've been missing.
The core mechanic repeats at every phase: AI proposes → Human approves → AI executes → Human approves → next phase. Every decision logged in git, permanently.
Requirements → Design → Unit Breakdown
Steering Docs capture intent. Stateful MCPs structure the requirement, propose design, and decompose into Units of Work — each with full context pre-loaded. Git Hooks block progression until the requirements PR is merged.
Context loaded → Code generated → Reviewed → Merged
Engineers claim Units of Work (Bolts) with context already there. MCPs propose functional design and generate code. Git Hooks enforce review at every checkpoint. Each Bolt ships as a sub-PR — ready to review, not interpret.
Deploy → Monitor → Respond → Every decision in git
MCPs generate the deployment plan and rollback procedures before anything ships. Git Hooks enforce approval gates. The Visual Orchestrator surfaces status across all Bolts — no standups required.
In Practice
Five UAT bugs.
One session. No coordination overhead.
Five structured requirements files, generated. Each one formatted, scoped, and agent-ready. The engineer receiving it knows exactly what done looks like before they open their IDE.
Structured and agent-ready. Context locked. No ambiguity to resolve mid-build.
Sub-PRs created per unit. Relevant context pre-loaded. Suggested code changes surfaced for engineer review — not generated and deployed. Surfaced and reviewed. Every approval is in git.
Tracked. Auditable. Engineering Lead sees all five — phase, status, who approved — without a standup.
Product isn't juggling four tools. The engineer isn't interpreting a vague ticket. The AI isn't guessing at intent. Engineering Leads can see all of it — without a standup.
What Teams Experience
When the process is installed, engineering stops being the constraint.
Since installing the AI-DLC Workspace, we're moving so fast that Product has become the bottleneck. They can't come up with new features fast enough to keep up with how quickly we can build them.
— Engineering Lead, Series B SaaS company
Choose Your Plan
One platform. Three ways in. Start building today.
SaaS
AI-DLC Workspace
$50/seat/mo
Unlimited seats. Full platform, no feature gates.
The complete AI-DLC infrastructure — Stateful MCPs, Steering Docs, Git Hooks, Visual Orchestrator, delivery metrics, and multi-squad dashboards. Everything you need to run the AI-DLC Standard.
Get StartedEnterprise
Workspace + Implementation
Custom
SSO/SAML, any git provider, VPC deployment.
Everything in Workspace + Orchestrator plus Expert AI-DLC Installation — a 90-day embedded engagement where we tune the infrastructure to your culture and stack, and stay through three delivery cycles.
Contact SalesEntry Point
Workshop + Workspace
$15K–$25K
2 days on-site or remote. Workspace licenses included.
Two days, your team, your actual repos. We install the AI-DLC Workspace, run all three phases on real work, and deliver a role-by-role breakdown with a customized implementation roadmap.
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Ready?
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. No slide deck. No sandbox. Real infrastructure, real delivery outcomes.
Questions? sales@simplygoose.com