Cardona CT LabAI Tooling · Design & Accessibility Whitepapers
Flagship Systems Whitepaper · September 2026

Forge Platform: Governed AI-Enabled Content & Production Systems

How structured content, bounded specialist tools, AI evaluation, human authority, accessibility, and stable contracts form one production architecture.


Executive Summary

Forge is an interconnected family of content and production systems built around one premise: complex creative work becomes more reliable when intent, structure, automation, specialist processing, review, accessibility, and publication share a governed model instead of being coordinated through disconnected files and tribal knowledge.

CourseForge is the structured authoring and governance center. ForgeScaffold turns planning data into deterministic production architecture. Art Request derives production briefs from the same source intent. ForgeClip keeps interactive-video behavior as portable structured data. ForgePack isolates heavyweight media processing. Forge3D models technical-art work as governed jobs with explicit validation and review.

Core operating model

Learn → Experiment → Evaluate → Integrate → Govern. AI becomes product capability only after its useful behavior, failure modes, workflow placement, permissions, and human-review boundaries are understood.

One Governed Source, Bounded Specialist Tools

01 · SOURCE
CourseForgeStructured intent, review, accessibility, publishing
02 · STRUCTURE
ForgeScaffoldDeterministic identifiers and production paths
03 · REQUEST
Art RequestProduction briefs derived from instructional need
04 · SPECIALISTS
Clip · Pack · 3DFocused transformations with explicit contracts

The architecture deliberately avoids two extremes: treating filenames/folders as the integration API, and forcing every specialist workflow into one universal application. The design target is bounded responsibility + explicit contracts + shared context.

Human ↔ Agent Authority

A model's ability to call a tool is not the same thing as authority to perform the action. Forge separates four states that AI demos often collapse:

01
Generate
Create candidate content or a proposed action.
02
Validate
Run schema, grounding, accessibility, and policy checks.
03
Human Approve
A person determines whether the work is acceptable in context.
04
Publish / Act
Operational authority becomes available only after the required state.
Authority principle

Tool availability is constrained by workflow state. Generation does not imply approval; approval does not automatically imply publication.

AI Evaluation: Failure Modes Become Product Constraints

The AI Evaluation Lab turns common model failures into reusable design constraints rather than treating them as prompt-writing accidents.

Reject / revise

Schema drift

Output looks plausible but violates the governed object contract or omits required fields.

Hard reject

Unsupported facts

Generated technical specificity is absent from supplied source evidence.

Revision

Accessibility gap

A useful media concept lacks text-equivalent intent or depends on color alone.

Authority block

Agent overreach

The proposed action is technically possible but outside the agent's permitted workflow state.

Reviewable

Validated output

Automated checks pass; the result becomes eligible for human approval, never silently approved.

The recruiter environment can run these cases as bounded deterministic fixtures and exposes schema, grounding, accessibility, authority, provenance, latency, and review requirements. Production actions remain disabled.

Architecture Explorer

The same platform can be inspected through four views. Switch below to see how responsibility changes without changing the underlying source model.

Product view

Responsibility is divided by workflow domain while shared context remains stable.

CourseForge

Authoring, governance, review, publishing.

ForgeClip

Interactive-video authoring as portable data.

ForgePack

Heavy media processing and QA.

Forge3D

Governed technical-art jobs.

Evidence Provenance

Portfolio claims should be inspectable. The Forge recruiter environment uses a simple evidence model:

Evidence chain

Claim → Artifact → Code → Decision → Verification

Recruiter guest access is read-only
Artifact
Guest recruiter environment
Code
server/auth.py · AuthControl.jsx
Decision
Read authentication is separate from write authorization.
Verification
Auth regression tests + Playwright production smoke gate.
One canonical content model drives downstream tools
Artifact
Forge Platform Orientation dataset
Code
recruiter_demo_seed.py · recruiter_demo.py · ForgeScaffold fixtures
Decision
Derive production structure from shared hierarchy instead of re-entering context.
Verification
Dataset + portfolio contract tests.
AI output remains human-governed
Artifact
AI Evaluation Lab
Code
AI evaluation routes + AIEvaluationLab.jsx
Decision
Generation, validation, approval, and operational authority are separate states.
Verification
Evaluation contract tests and bounded-run checks.

Design → Code Evidence

The recruiter role-path card is a worked example of how product intent survives implementation.

01
Design intent
Recognize a role and enter the right evidence path quickly.
02
Semantic tokens
Amber emphasis, raised surfaces, borders, technical mono labels.
03
States
Default, hover/focus, active application lens, keyboard focus.
04
React
Role data renders from seeded contracts rather than screenshots.
05
Accessibility
Native buttons, visible focus, textual active state, Escape/focus containment.
06
Production
Deep links, tests, lazy loading, and live seeded role data.

Accessibility as Production Architecture

Accessibility requirements travel with the work instead of appearing only at final QA. Alt-text intent belongs on visual requests. Captions and transcript expectations belong with time-based media. Focus, keyboard operation, contrast modes, reduced motion, and state labels are component/system responsibilities.

Shift in operating model

Move from “test the finished asset” to “define what the asset and workflow must support before production begins.”

Operational Quality

A portfolio used in hiring is still a production system. The Forge environment treats reliability as part of the evidence.

Contract QA

Seed data, API schemas, role lenses, provenance, AI evaluation, architecture, and design-system behavior are regression tested.

Security boundary tests

Recruiter guest reads succeed while writes remain blocked; public portfolio routes do not expose authoring APIs.

Production smoke gates

Playwright and live smoke runners verify recruiter paths and linked specialist tools before applications are sent.

Performance budgets

Recruiter-only evidence is code-split and the initial CourseForge bundle is guarded in CI.

Portfolio Health

Owner-only health checks surface fixture version, migration state, deployment commit, and linked service status.

Interactive Evidence

Closing Principle

Forge is less about a particular LCMS, media processor, or AI feature than it is about a way of designing systems. Complex work crosses disciplines, tools, and levels of automation. The architecture therefore emphasizes explicit structure, bounded responsibility, stable contracts, visible state, accessibility, human authority, and evidence.

Forge

Reduce friction without hiding responsibility.