Templates/AI Agent Workflow

AI Agent Workflow

AI / MLAdvancedAgentic automation

Autonomous AI agent with LLM inference, RAG retrieval, tool execution, async job queue for long tasks, human approval checkpoint, and persistent memory. Use for copilots, autonomous workflows, and AI agents.

Recommended for: Teams orchestrating tool-using AI agents

9 nodes10 connectionsAgent orchestrationTool callingMemory persistence

Scope Notes

Intended for Teams orchestrating tool-using AI agents at Advanced complexity and Agentic automation scope. Treat the graph as a starting shape rather than a fixed stack: keep the topology and the signals below, then swap providers to match your platform.

Agent orchestrationTool callingMemory persistence

Review Checklist

0 of 12 reviewed

Use Case

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

Best Fit Scenarios

  • Workflow copilots with approval checkpoints
  • Automation requiring retrieval plus tool execution
  • Agent products that need persistent memory and audit trails

Stack Breakdown

Chat UIOrchestratorLLM ProviderVector DBTool ServerJob Queue

Architecture Layers

1Conversational UI
2Agent orchestrator
3LLM inference
4RAG retrieval
5Tool execution
6Memory, queue & approval

Components by Category

frontend

Chat UIReview Console

backend

OrchestratorTool Server

external

LLM Provider

database

Vector DBPostgreSQLRedis

async

Job Queue

Why This Topology Works

The orchestrator runs the agent loop — prompt, LLM, tool dispatch, response. Vector DB retrieval grounds answers, PostgreSQL keeps memory across sessions, Redis caches the context window, and long-running tool work is queued so the loop is not blocked.

Scaling Notes

The orchestrator is stateless and scales horizontally. LLM calls are the latency bottleneck, so route by model and queue the long tail. Vector indexes scale by partitioning embeddings.

Observability

Track tokens per request, retrieval relevance, tool-call success rate and end-to-end latency from prompt to response. Sentry-like error capture is not drawn — add it before production.

Typical Bottlenecks

  • LLM latency and token cost per agent step
  • Runaway tool loops exhausting the queue
  • Memory growth across long-lived sessions

Async Flow and Reliability

The orchestrator handles the interactive loop, but long-running tool executions go through the job queue and return results asynchronously, keeping the conversational turn responsive.

Upgrade Path

Add an auth boundary for per-user memory scoping, a sandbox around untrusted code execution, and a DLQ for timed-out tool calls. Add a policy engine to restrict which tools each user may invoke.