architecting-innovation-agents
Propose multi-agent and workflow architectures for CustomGPT.ai Labs projects, showing how Claude Code, CustomGPT.ai, and supporting tools interact to deliver the desired business outcome.
When & Why to Use This Skill
This Claude skill specializes in architecting multi-agent systems and innovation workflows. It translates Product Requirement Documents (PRDs) into detailed technical blueprints, helping teams decide between RAG and multi-agent approaches while ensuring seamless integration between CustomGPT.ai, Claude Code, and existing business infrastructure. It is designed to accelerate the transition from concept to technical design review with a focus on practical implementation.
Use Cases
- Transforming an Innovation PRD into a high-level agent and system architecture for technical design reviews.
- Evaluating and selecting the optimal workflow strategy, such as choosing between simple RAG and complex multi-agent systems.
- Mapping data and control flows between CustomGPT.ai, Claude Code, and external services like CRMs or data warehouses.
- Designing rapid AI prototypes with a focus on 2-4 week implementation timelines and explicit technical tradeoffs.
| name | architecting-innovation-agents |
|---|---|
| description | Propose multi-agent and workflow architectures for CustomGPT.ai Labs projects, showing how Claude Code, CustomGPT.ai, and supporting tools interact to deliver the desired business outcome. |
Architecting Innovation Agents
You turn an Innovation PRD into a high‑level agent and system architecture suitable for a design review.
When to Use
Use this skill when the user:
- Needs a technical approach for an Innovation project.
- Is deciding between simple RAG vs. multi‑agent workflows.
- Wants to understand how CustomGPT.ai, Claude Code, and other services should work together.
Inputs
Expect:
- The project PRD or equivalent description.
- Any explicit technical constraints (hosting, auth model, data residency, must‑use components).
- Notes on existing components (CustomGPT.ai chat widget, AI call center, CRMs, data warehouses, etc.).
Architecture Output
Produce a Markdown document with:
- Overview – one short paragraph summarizing the architecture choice.
- Agents and Components – a numbered list where each item has:
- Name and role.
- Responsibilities.
- Inputs and outputs.
- Data & Control Flow – step‑by‑step description of how a typical request flows through the system.
- Context & Memory – how RAG sources, metadata, and history are loaded and updated.
- Safety & Compliance – where security, policy enforcement, and human overrides sit in the flow.
- Implementation Notes – what should be implemented via CustomGPT.ai config, Claude Code automation, or traditional backend code.
If the user asks, also include a simple ASCII or Mermaid diagram of the flow.
Guidelines
- Prefer the simplest architecture that can support the experiment or V0 within 2–4 weeks of effort.
- Make tradeoffs explicit (quality vs. latency, flexibility vs. complexity).
- Call out assumptions that engineering must validate.