### High Level Needs
  * AI models are accessed ad hoc without standardization or documentation, relying heavily on the architect's knowledge for functionality use.
  * Basic documentation and a standard AI Access exists for core functionalities, with limited and inconsistent access control.
  * Standardized, well-documented APIs provide comprehensive access to AI services, supported by established AI Access management practices.
  * AI Access is monitored with metrics, managed proactively for performance and security, and designed with versioning and backward compatibility.
  * AI Access is continuously optimized with dynamic scaling, self-healing, and enhancements driven by active developer on the product teams.

### Key Concepts
  * A foundational framework for interacting with AI models and services via standardized interfaces.
  * Ensures consistency, scalability, and reliability across AI functionalities.
  * Provides abstraction to simplify model interactions, enhance developer experience, and improve integration with business systems.
  * Focuses on access control, performance monitoring, and ensuring backward compatibility.
  * Includes practices for proactive security, documentation, and versioning.
  * Introduces advanced capabilities such as dynamic scaling, self-healing APIs, and continual improvement via developer feedback

### Expected Benefits
  * Standardization: Ensures consistent access and interaction with AI services, minimizing redundancy and enhancing integration.
  * Performance & Scalability: Eases the scaling of AI capabilities to fit organizational needs and allows for updates without major interruptions.
  * Security & Governance: Improves data security and compliance through access control and monitoring.
  * Developer Productivity: Enhances usability with thorough documentation and standardized APIs. API versioning facilitates easy transitions during upgrades.
  * Cost Accounting: Establishes a centralized method for tracking AI-related expenses.

If the goals are user centric, use a User Story as the output.

If the goals are technology, system, engineering, or tool centric, use a Requirement. 

Format all the User Stories, and Sub-User Stories in the following format: 

"As a [User Role Who would pay to use the app to do something], I want to [Action the User wants to do in a narrative format that tells a story], so that [Outcome, Benefit, or Value Created and complete the narrative story]."

Following each User Story and Sub-User Story include Acceptance Criteria that is SMART using the following format:

"[Scenario: A labor for the behavior being described]: Given [The Starting Condition for the scenario to test, include any preconditions, environmental details, or relevant information], When [A specific action that the user takes or an automated process takes within the system takes]. Then [The expected outcome of the "When", which could be used as confirmation that something happened correctly or a failure of it] And [Chain together up to three Given, When, Then statements]."

Format all the Requirements, and sub requirements in the following format:

"The [System that this requirement is assigned to] [Shall {for requirements} | Will {for facts or declaration of purpose} | Should = {for goals}] [Do some capability or create some business outcome] while [some set of conditions need to be met that can be measured] [under some measurable constraint]

Each requirement / user story must include:

1. Verification Methodology
“Did we build the system right?” Verification confirms that the system meets specified requirements.  Please select from the following which approach should be taken.
1. **Inspection**
    * Manual review of documents, code, models, drawings, or hardware.
    * Checks conformance to standards or requirements.
    * Example: Peer review of system design documents.
2. **Demonstration**
    * Functional operation under specified conditions.
    * Typically qualitative and observable.
    * Example: Pressing a button to verify the system powers up.
3. **Test**
    * Quantitative, measurable performance validation under controlled conditions.
    * May be conducted at component, subsystem, or system level.
    * Example: Thermal vacuum test on satellite components.
4. **Analysis**
    * Use of mathematical models or simulations to verify performance.
    * Often used when physical testing is impractical.
    * Example: Structural finite element analysis for stress/strain.
5. **Model-Based Verification**
    * Verification via formal modeling and simulation (SysML, MBSE tools).
    * Enables early lifecycle verification.
    * Includes model checking and simulation-based validation of behavior and interfaces.
6. **Automated Verification**
    * Use of software tools to execute scripted tests and verify compliance.
    * Often used in software-intensive systems.
    * Example: Unit testing frameworks, static code analysis tools.

1. Validation Methodoolgy
“Did we build the right system?” - Validation ensures the system meets stakeholder needs and intended use. Please select from the following which approach should be taken.
1. **Operational Testing**
    * Involves users operating the system in its intended environment.
    * Focused on end-to-end performance and user satisfaction.
    * Example: Flight testing of a new aircraft by experienced pilots.
2. **Simulations and Emulation**
    * High-fidelity models or emulators replicate real-world conditions.
    * Useful when full system deployment is not yet possible.
    * Example: Power grid simulation for control software.
3. **Prototyping**
    * Building an early or partial version to validate concepts or user needs.
    * Can be physical or digital (mock-ups, wireframes, MVPs).
    * Example: Prototype of a medical device evaluated by clinicians.
4. **Stakeholder Review / Walkthroughs**
    * Direct engagement with stakeholders to confirm the solution aligns with their intent.
    * Structured interviews or walkthroughs of system concepts or interfaces.
5. **Field Trials / Pilots**
    * Limited deployment in operational context with real users.
    * Helps assess readiness, usability, and integration with business processes.
    * Example: Pilot rollout of new grid management software in a single region.
6. **Human-in-the-Loop Testing**
    * Integrates human decision-making into simulations or operations.
    * Assesses ergonomics, workflow compatibility, and cognitive load.

1.  **INVEST Criteria**  
For each story, confirm the following attributes. Stories may be annotated with ☑️ (Yes), ❓ (Unclear), or ❌ (No) to guide iteration:

- [ ] **Independent** – Can be scheduled and delivered in any order without creating blockers. Stories should follow the Single Responsibility Principle.
- [ ] **Negotiable** – Written to invite conversation, not act as fixed contracts. Only include known, high-confidence details.
- [ ] **Valuable** – Delivers observable value to a specific user or stakeholder (e.g., end-user vs. purchaser). Prefer thin vertical slices of functionality over isolated technical layers.
- [ ] **Estimable** – Sized and described well enough that the team can provide a meaningful effort estimate. If not, consider splitting or inserting a Spike.
- [ ] **Small** – Fits within a single iteration or <1–2 person-weeks. Too big? Slice it until the scope feels clear and actionable.
- [ ] **Testable** – Clearly defined success criteria. If it can’t be tested, it’s either not ready or not specific enough.

4. Time Estimate: e.g., “~2 days for 2-person UX team”

5. Confidence Score (1–5):

1 = Low (many unknowns or vague input)
3 = Moderate (acceptable but incomplete)
5 = High (fully scoped and realistic)

6. Strategy Summary
Conclude with a short explanation of your planning logic (3 – 5 sentences).
Add an optional TL;DR for non-technical stakeholders.
Label each user story with complexity:basic or complexity:advanced where useful. Suggest escalating from basic to advanced only when warranted.