Yes. We can generate the complete design package for a Custom GPT, workspace agent, or API-deployed agent. The correct output depends on where it must run, what it can access, how it is governed, and whether it must act outside ChatGPT.
“GPT” is now one member of a larger assistant portfolio. Starting with the deployment boundary prevents an elegant configuration from becoming the wrong product.
A no-code, purpose-specific version of ChatGPT. It combines instructions, knowledge, built-in capabilities, and either apps or custom actions.
Best for: guided expertise, repeatable analysis, internal reference assistants, public GPT Store experiences, and lightweight API integration.
A shared organizational agent for repeatable workflows. Current OpenAI documentation describes support for tools, apps, custom MCPs, skills, files, schedules, channels, and API triggers.
Best for: operational workflows, scheduled work, Slack participation, internal automation, and governed multi-system execution.
A developer-built solution deployed in a website, service, or internal platform. Agent Builder supports visual workflow construction, preview, and export to code.
Best for: embedded experiences, platform APIs, identity-bound authorization, custom telemetry, deterministic orchestration, and production-scale control.
| Decision question | Custom GPT | Workspace agent | API agent |
|---|---|---|---|
| Primary runtime | ChatGPT | Managed ChatGPT workspace and connected channels | Your application or service |
| Creation model | Web-based GPT builder | Workspace agent builder | Agent Builder, SDKs, or custom code |
| External execution | Apps or REST actions | Apps, tools, MCP, schedules, channels, API triggers | Fully controlled tools, APIs, queues, and workflow engines |
| Identity and authorization | Workspace policy plus action/app authorization | Workspace RBAC and connected-system permissions | Application identity, policy engine, claims, entitlements, and service authorization |
| Public embedding | No. GPTs run in ChatGPT. | No. Designed for managed workspace use. | Yes. Build into websites and products. |
| Operational control | Moderate | High within workspace controls | Maximum, with corresponding engineering responsibility |
The durable deliverable is not one prompt. It is a versioned assistant package with separated behavior, knowledge, integration, evidence, and governance layers.
A robust GPT is a layered system. Mixing the layers creates prompt drift, weak retrieval, accidental authority, and hard-to-test behavior.
Communicates what the GPT is for, who should use it, and how to begin. It should set expectations before the first prompt.
Defines the invariant operating contract. Rules, tone, decision logic, evidence handling, and output formats belong here—not in uploaded knowledge.
Supplies source material. It should be modular, text-forward, versioned, and designed for retrieval rather than visual presentation.
Adds tools. Every capability expands utility and the attack surface. Enable only capabilities tied to a validated user outcome.
Proves expected behavior across normal, ambiguous, adversarial, stale-data, authorization, and dependency-failure conditions.
OpenAI currently documents up to 20 knowledge files per GPT, with a maximum of 512 MB per file. Text and document files are also subject to token limits. Complex visual layouts can reduce retrieval quality.
Effective instructions are explicit, positive, structured, testable, and paired with examples. OpenAI’s current guidance recommends clear step structure, concrete directives, headings, and examples for classifications.
State the role, primary outcome, target audience, and the conditions under which the GPT is useful.
Define supported tasks, excluded tasks, source precedence, and when the GPT must distinguish facts, inference, and recommendation.
Describe the sequence from intake to classification, retrieval, reasoning, tool use, validation, response, and follow-up.
Specify when each tool is required, forbidden, optional, or requires confirmation. Describe minimum data sent to third parties.
Define ordering, structure, evidence expectations, citations, uncertainty language, schemas, and artifact quality requirements.
Explain what to do when knowledge is missing, tools fail, permissions are insufficient, sources conflict, or the user asks for an unsupported operation.
# Role
You are [specific role] for [audience]. Your primary outcome is [measurable outcome].
# Scope
Support:
- [task]
- [task]
Do not claim authority for:
- [excluded domain]
- [operation requiring another system]
# Source policy
1. Prefer [canonical source class].
2. Use uploaded knowledge for stable reference material.
3. Use live tools for time-sensitive state.
4. Label facts, assumptions, inferences, recommendations, and unresolved questions.
5. Cite sources using [required format].
# Operating workflow
When a request arrives:
1. Classify the user outcome and risk.
2. Identify missing information that materially changes the result.
3. Retrieve only the minimum relevant knowledge.
4. Use tools according to the tool policy.
5. Validate claims, permissions, and output structure.
6. Lead with the decision or result when evidence supports it.
7. State limitations and the next action.
# Tool policy
- [Tool]: use when [trigger].
- [Tool]: require confirmation before [consequential action].
- Send only the minimum necessary data to external services.
- Never represent a failed tool call as a completed action.
# Output contract
- Use [format].
- Include [evidence / trade-offs / controls].
- Keep terminology consistent with [domain vocabulary].
- For consequential recommendations, include alternatives and success criteria.
# Failure and recovery
- Missing evidence: state the gap and what would resolve it.
- Conflicting sources: present the conflict and source precedence.
- Tool failure: preserve partial work, report the failure, and provide a recovery path.
- Insufficient permission: do not route around authorization controls.
# Examples
## Acceptable
[representative input and expected properties]
## Unacceptable
[representative failure and why it fails]Tools should follow the user outcome and assurance model. A GPT can use apps or custom actions, but not both at the same time.
Current documented options include web search, image generation, Canvas, and Code Interpreter & Data Analysis. Availability varies by account, workspace, and region.
Apps let the GPT use tools and services connected by the user. Workspace controls and user confirmation may apply. Apps can also constrain public sharing.
Actions connect to REST APIs through OpenAPI JSON or YAML. Authentication options include none, API key, and OAuth.
In managed workspaces, GPT availability, creation, sharing, integrations, and ownership are policy-controlled. Governance cannot be retrofitted reliably after adoption.
Declare allowed data classes, prohibited data, cross-border constraints, and whether content may leave the workspace through an app or action.
Scope actions to the smallest operation and OAuth scope. Avoid shared credentials where a user-bound authorization model is required.
Define confirmation thresholds, reversible versus irreversible actions, escalation owners, and recovery procedures.
Name a product owner, knowledge owner, integration owner, risk approver, and support path. A GPT without an accountable owner is an unmanaged service.
Preview is necessary but insufficient. OpenAI recommends representative evaluation questions; an enterprise release should add adversarial, authorization, dependency, and regression coverage.
Representative tasks meet the agreed success rubric. Critical use cases have deterministic expected properties, not merely “looks good.”
Consequential claims are sourced, source precedence is respected, and the GPT does not invent citations or silently merge conflicting versions.
Read and write paths enforce intended identity, entitlements, confirmation, domain allowlists, and least-privilege scopes.
Tool failure, rate limiting, stale dependencies, and partial completion produce truthful status and a recoverable next step.
Responses remain usable on mobile, preserve hierarchy, avoid misleading confidence, and meet required accessibility and artifact quality standards.
Ownership, support, version, rollback, knowledge refresh, audit, and retirement criteria are documented before broad release.
The workflow begins with an outcome and ends with an owned, testable service. Tool selection occurs only after the communication and operating model are clear.
Define user, decision, task, frequency, risk, evidence needs, success measure, and what must remain human-owned.
Choose Custom GPT, workspace agent, or API agent based on deployment, integration, authorization, scheduling, channel, and operational requirements.
Separate concepts, sources, rules, workflows, tools, outputs, exceptions, risks, and ownership before writing instructions.
Generate metadata, instructions, knowledge corpus, integration schemas, privacy notes, evaluation fixtures, and release checklist.
Transfer the package into the builder, validate capabilities, connect integrations, test real prompts, and tighten instructions before adding more tools.
Run functional, evidence, injection, authorization, failure, and presentation tests. Repair failures before publishing.
Start private, then invite-only or limited workspace access, then broaden only after telemetry and user behavior support the decision.
Review analytics, incidents, unanswered questions, action failures, knowledge freshness, and regression results. Version every material change.
The strongest first candidate is a focused Custom GPT that packages the accumulated HTML, mobile-first, accessibility, communication, and artifact-quality system into a repeatable design-and-audit assistant.
Turn research, requirements, and rough HTML into durable, mobile-first, accessible, single-file documentation or application artifacts with explicit validation and remediation.
The GPT should adapt the same knowledge model for executives, architects, engineers, designers, and content owners without fragmenting the source of truth.
This manifest is not a native OpenAI import format. It is a durable source-of-truth representation that can generate the builder fields, knowledge bundle, action schema, tests, and release record.
schemaVersion: "1.0"
kind: custom-gpt-package
metadata:
id: html-systems-architect
name: HTML Systems Architect
owner: Jesse Graupmann
version: 0.1.0
status: draft
generatedAt: 2026-07-21T20:24:55-04:00
provenance:
method: AI-assisted research, synthesis, implementation, and red-team review
sources:
- OpenAI Help Center
- OpenAI Developer Documentation
product:
outcome: >-
Generate and audit durable, mobile-first, accessible HTML artifacts
using evidence-backed communication and UI engineering standards.
audiences:
- executives
- architects
- engineers
- designers
deploymentBoundary: custom-gpt
exclusions:
- unattended production deployment
- bypassing workspace authorization
- representing unverified research as fact
builder:
description: >-
Designs, rebuilds, and audits professional single-file HTML experiences
with mobile-first layout, accessibility, provenance, and release validation.
conversationStarters:
- Audit this HTML for overflow, sticky-header collisions, alignment, and contrast.
- Turn this research into a self-contained mobile-first HTML handbook.
- Red-team this interface against our artifact quality release gates.
- Rebuild this page with a calibrated reset and deliberate spacing system.
capabilities:
webSearch: true
imageGeneration: false
canvas: true
codeInterpreter: true
integrationMode: none
instructions:
source: instructions.md
requiredPolicies:
- separate facts, assumptions, inferences, recommendations, and open questions
- plan communication before choosing presentation
- validate runtime interactions before release
- enforce component-level WCAG 2.2 AA contrast
- preserve provenance and generated timestamp
knowledge:
files:
- 00-domain-map.md
- 10-mobile-first-layout.md
- 20-native-html-and-css.md
- 30-accessibility-and-aria.md
- 40-visual-hierarchy-and-typography.md
- 50-artifact-quality-battle-scars.md
- 60-provenance-and-metadata.md
- 90-release-checklist.md
assurance:
evaluationSuite: evals/gpt-evals.yaml
releaseThresholds:
criticalFailures: 0
highSeverityFailures: 0
representativeTaskPassRate: ">= 0.90"
citationFidelity: ">= 0.95"
requiredGates:
- functional
- evidence
- authorization
- resilience
- accessibility
- responsive-layout
- lifecycle
release:
initialVisibility: private
rollout:
- creator-preview
- invited-reviewers
- limited-workspace
- broader-workspace
rollback: restore prior GPT version and revalidate action authentication if used
reviewCadence: monthly-or-material-changeProduct facts were verified against official OpenAI documentation available on July 21, 2026. Architectural recommendations are synthesis and should be validated against workspace policy, legal requirements, and the specific integration.
Definition, availability, components, apps versus actions, privacy, conversation access, and external embedding boundary.
Web-only creation, configuration fields, knowledge limits, capabilities, preview, and version history.
Authentication options, OpenAPI schema requirements, action-domain restrictions, privacy policies, and approval behavior.
RBAC, sharing, app controls, action-domain allowlists, collaboration, ownership continuity, compliance, and analytics.
Private, link, workspace, and GPT Store sharing requirements, including public-action privacy policy requirements.
File retention, deletion behavior, visual retrieval differences, and business-data training treatment.
Capability availability, apps/actions conflicts, publishing blockers, file generation requirements, and escalation evidence.
Action execution model, REST integration, function calling, and retrieval versus write use cases.
Schema, authentication, action testing, OAuth callback, and representative evaluation guidance.
Agent builder, tools, apps, custom MCPs, skills, files, schedules, channels, and API triggers.
Visual workflow composition, live preview, external deployment, and code export.