business automation system architect
Transform manual processes into production-grade automation blueprints with AI logic, risk controls, and recovery paths.
$5
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Example session with this skill installed
I need to automate our client onboarding. It starts with a Typeform, then we manually check their LinkedIn, draft a personalized welcome email in Gmail, and create a folder in Google Drive.
- Read your context and instructions
- Compiled the business automation system
I have designed a production-ready blueprint for your client onboarding automation, focusing on data persistence and human-in-the-loop verification for the personalized outreach.
Client Onboarding Automation Blueprint
| Component | Logic Type | Description |
|---|---|---|
| Trigger | Deterministic | Typeform Webhook (New Entry) |
| Data Store | System | Log initial payload to Airtable/Database (Status: Pending) |
| Research | AI | Extract LinkedIn URL and summarize recent activity/posts |
| Drafting | AI | Generate draft email based on Typeform interests and LinkedIn summary |
| Human Gate | Manual | Review/Edit email draft in a custom UI or Slack block |
| Execution | Deterministic | Send Gmail; Create Google Drive Folder; Update Status to "Active" |
System Components & Architecture
graph TD
A[Typeform] --> B{Entry Handler}
B --> C[Airtable Log]
B --> D[AI Research Agent]
D --> E[AI Draft Engine]
E --> F[Slack Approval Notification]
F -- Approved --> G[Gmail API]
F -- Approved --> H[Google Drive API]
G & H --> I[Update Airtable Status]
F -- Rejected --> J[Manual Rework]
Risk Controls & Resilience
Idempotency: Use the Typeform submission_id as a unique key to prevent duplicate folder creation if the webhook retries.
AI Fallback: If LinkedIn scraping fails or profile is private, the system flags the record for manual research instead of halting the flow.
- Retry Logic: Implement exponential backoff for Google Drive API calls to handle rate limits.
- Audit Log: Every AI-generated draft is stored alongside the final sent version for quality auditing.
Next steps
- Select a middleware (Make.com or n8n) to host the webhook and state logic.
- Define the specific prompt templates for the LinkedIn summarization and email drafting steps.
- Set up a Slack channel or simple dashboard for the human approval step.
Connects securely to your tools. The creator never sees your data.
About this skill
Manual workflows often break when scaled because they lack error handling, state management, and clear boundaries between AI and logic. This skill converts informal processes into production-ready blueprints that account for real-world failure modes.
What it does
- Workflow Mapping identifies current actors, systems, and source-of-truth boundaries to find bottlenecks.
- State Design defines explicit transitions and future-state workflows to ensure data integrity.
- Logic Separation isolates deterministic business rules from probabilistic AI tasks like extraction or drafting.
- Resilience Engineering specifies retry logic, idempotency, logging, and monitoring requirements for stable operations.
- Risk Control integrates human-in-the-loop approvals for high-stakes or irreversible actions.
How it works
- Provide your current manual process, the systems involved, and your desired outcome.
- The agent maps the "as-is" state and identifies critical pain points.
- It designs a future-state architecture separating AI steps from hard-coded business rules.
- It layers in technical requirements for retries, fallbacks, and authentication.
- You receive an implementation plan covering both an MVP and a robust production path.
Frameworks & tools
This skill is tool-agnostic and applies to platforms like Make, n8n, Zapier, or custom microservices using Python and Node.js.
Why this beats prompting it yourself
Generic prompts often ignore what happens when an API fails or an AI hallucination occurs. This skill forces production-minded constraints like idempotency and recovery paths into every design.
Use cases
- Automating invoice processing that requires both OCR and human financial approval.
- Scaling lead qualification workflows from raw inbound signals to CRM entry.
- Building content supply chains that combine AI drafting with multi-stage editorial review.
- Designing customer support escalations that sync data between helpdesks and engineering trackers.
Known limitations
This skill designs architectures but does not execute code, configure API keys, or perform live deployments.
Architectural design only. Does not perform live API calls, credential management, or direct deployment to automation platforms.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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