Templates/AI Agent Workflow

AI Agent Workflow

AI / ML

LLM orchestration with tool calls, vector retrieval, human-in-the-loop, and memory

7 nodes7 connections

Use Case

AI copilots, autonomous research agents, customer support bots, code assistants

Stack Breakdown

Chat UIOrchestratorGPT-4 / ClaudeVector DBPostgreSQL

Architecture Layers

1Conversational UI

2Agent Orchestrator

3LLM Inference

4RAG Retrieval

5Tool Execution

6Memory Store

Components by Category

frontend

Chat UI

backend

OrchestratorTool Server

external

LLM Provider

database

Vector DBPostgreSQLRedis

Why This Topology Works

The orchestrator runs an agent loop: prompt → LLM → tool dispatch → response. Vector DB enables RAG retrieval for grounded answers. PostgreSQL persists conversation memory across sessions.

Scaling Notes

Orchestrator is stateless and scales horizontally. LLM calls are the bottleneck — use request queuing and model routing. Vector DB indexes scale with embedding partitioning.

Observability

Track tokens per request, retrieval relevance scores, tool call success rates, and end-to-end latency from prompt to response.