AI-Powered Work as a Service — Architecture
System Architecture v1.0

AI-Powered Work as a Service

Agentic AI Infrastructure — Automation · RAG · Memory · Security

ENTRY 💬
Client Workspace
Dedicated AI Automation Platform instance per client. Isolated workflows, custom triggers, restricted credential scope.
INPUT 🌐
Webhook Triggers
HTTP endpoints, form submissions, scheduled crons, and event-based activations.
CHANNEL 📱
Communication Channels
Slack, WhatsApp, Email, Telegram — client-facing bots and notification pipes.
ACCESS 🔐
RBAC Permissions
Role-based access. Clients see only their workflows. No visibility into private data layers.
Trigger Nodes
Router / Switch
HTTP Request
Code Node (JS)
AI Agent Node
Tool Nodes
Output / Respond
Error Handler
🤖 Agentic AI Orchestrator
Multi-Agent System · Tool Use · Memory · Planning · Reflection
Research Agent
Content Agent
Data Analyst Agent
Outreach Agent
Support Agent
Operations Agent
Supervisor Agent
VECTOR 🗂️
Vector Store (RAG)
Pinecone / Qdrant / Supabase pgvector. Private embeddings, disconnected from client access.
MEMORY 💾
Conversation Memory
Redis / Zep for short-term session memory. Postgres for long-term structured memory.
DOCS 📄
Knowledge Base
Indexed documents, SOPs, client playbooks. Retrieved via semantic similarity search.
LOGS 📊
Execution Logs
Automation platform execution history (cleared before client handover). Audit trail for debugging.
🔒 Security & Isolation
Credential Vault Isolation
Client RBAC Restrictions
Execution Log Cleanup
Webhook Auth Tokens
No Cross-Client Data Bleed
Owner-Only Private Workflows
Env Var Secrets
DELIVER 📤
Automated Deliverables
Reports, summaries, emails, content drafts — auto-generated and delivered to clients.
NOTIFY 🔔
Notifications & Alerts
Status updates, task completions, anomaly alerts via Slack, email, or webhook.
STORE 🗄️
Data Outputs
CRM updates, spreadsheet writes, database inserts — structured results from agent tasks.
HUMAN 👤
Human-in-the-Loop
Approval gates, review steps, escalation paths when agent confidence is low.
📦 Service Delivery Model
Clients get isolated AI Automation Platform workspace — no access to owner infrastructure
RAG, memory, and private docs always remain under owner credentials
Agents run server-side; clients only see inputs and outputs
Modular: swap LLMs, vector stores, memory backends without client disruption
Execution logs cleared before client handover for full privacy
AI Automation Claude API Agentic RAG
🔄 Agent Capability Stack
Tool use: web search, code execution, API calls, file I/O
Memory: session context, long-term recall, user preferences
RAG: retrieve relevant docs before each LLM call
Planning: multi-step task decomposition and self-correction
Supervisor agent routes tasks to specialist sub-agents
Tool Use Memory Planning Reflection
Client-Accessible
AI Agent Core
Private / Owner Only
Orchestration
Security Boundary