---
type: source
title: "Why is Everyone Missing This with AI Agents?! (Memory + Tools that Scale)"
description: "To scale AI agents to thousands or millions of users, each user needs personalized, per-user tools and memory, which requires just-in-time agent authorization (via Arcade) and per-user long-term memory (via LangGraph)."
youtube_id: F1I9JN0z0w0
url: https://www.youtube.com/watch?v=F1I9JN0z0w0
slug: why-is-everyone-missing-this-with-ai-agents-memory-tools-that-scale
published: 2025-07-30
duration: "0:28:03"
recency_rank: 78
raw: "../raw/why-is-everyone-missing-this-with-ai-agents-memory-tools-that-scale.md"
tags: [multi-tenant, memory, agent-auth]
created: 2026-07-21
updated: 2026-07-21
---

# Why is Everyone Missing This with AI Agents?! (Memory + Tools that Scale)

Cole argues that most AI agents and MCP servers are built for a single user (one Gmail account, one Slack, one RAG knowledge base) with credentials effectively hardcoded, and that this design fundamentally will not scale. To serve thousands or millions of users, an agent must dynamically obtain per-user access to services and maintain memory unique to each user. He demonstrates this with an email-assistant agent built on LangGraph and Arcade, wrapped in a Streamlit interface with Supabase sign-in.

Arcade solves the agent-authorization problem: the agent dynamically requests OAuth access to a user's accounts (Gmail, Asana, and others) with scoped permissions, caches those credentials so it never has to ask again, and crucially requests authorization just in time, the first moment it needs a given tool on that user's behalf. This is wired into LangGraph as an extra authorization node using LangGraph interrupts. When the agent decides to call a tool that requires auth it does not yet have, the graph pauses, surfaces an OAuth URL, waits for the user to authorize, then resumes and continues to the tool call without re-running anything.

The build progresses in three stages: a basic Arcade tool-manager script, a from-scratch LangGraph graph (agent node, decision/should-continue node, authorize node, tool node) that shows the reusable authorization pattern, and finally a Postgres-backed version adding per-user long-term memory. Memory uses a LangGraph store namespaced by a unique user ID (the Supabase login email); a remember keyword triggers storing the message, and stored memories are fetched and injected into the agent's prompt on later turns. The result is hyper-personalized agents where every user gets distinct tools, distinct memory, and effectively their own per-user system prompt, letting a single agent scale to millions.

## Concepts covered

- [Multi-Tenant Agents](../concepts/multi-tenant-agents.md)
- [Agent Security](../concepts/agent-security.md)
- [Human in the Loop](../concepts/human-in-the-loop.md)
- [Memory Systems](../concepts/memory-systems.md)
- [Agentic Workflow Engineering](../concepts/agentic-workflow-engineering.md)
- [The Problems With MCP Servers](../concepts/mcp-problems.md)

## Entities

- [Arcade](../entities/tools/arcade.md)
- [LangGraph](../entities/tools/langgraph.md)
- [LangChain](../entities/tools/langchain.md)
- [n8n](../entities/tools/n8n.md)
- [Streamlit](../entities/tools/streamlit.md)
- [Supabase](../entities/tools/supabase.md)
- [Postgres](../entities/tools/postgres-pgvector.md)
- [Mem0](../entities/tools/mem0.md)
- [OpenAI](../entities/organizations/openai.md)
- [Jira](../entities/tools/jira.md)
- [Cole Medin](../entities/people/cole-medin.md)

## Key moments

- **[0:00:00]** Thesis: most agents/MCP servers are built for a single user and won't scale; agents need per-user personalization of memory and tools
- **[0:00:58]** The core problem shown in n8n: Gmail credentials are hardcoded into the agent, so adding a second user means duplicating the agent
- **[0:02:19]** Introduces Arcade as the key to solving auth/scaling: dynamic per-user OAuth, scopes, cached credentials, custom tools, just-in-time permission
- **[0:03:11]** Live demo of the LangGraph + Arcade agent with a Streamlit UI; access revoked to show the first-time auth flow
- **[0:04:52]** Just-in-time authorization demonstrated: Asana auth is only requested the moment the agent first needs the Asana tool
- **[0:05:44]** Long-term memory demo: 'remember' keyword stores tasks, recalled in a brand-new conversation
- **[0:06:34]** Different user sign-in gets a fully separate auth flow and separate memory, showing per-user isolation at scale
- **[0:11:03]** Arcade tool manager: get_tools with Gmail/Asana toolkits, to_langchain conversion, bind to a ChatOpenAI instance
- **[0:13:35]** LangGraph graph structure: agent node routes to tools or authorization based on whether the user already has access
- **[0:14:00]** LangGraph interrupts: at the authorization node the graph pauses to wait for the user to authorize, then resumes
- **[0:18:12]** should_continue decision node asks the Arcade manager whether a tool requires auth and whether the user already has it
- **[0:23:01]** Third example adds Postgres for checkpointers and a LangGraph store for long-term memory namespaced per user
- **[0:26:34]** Payoff: each user gets an individual system prompt via memory, avoiding one massive shared prompt

## Transcript

[Raw transcript](../raw/why-is-everyone-missing-this-with-ai-agents-memory-tools-that-scale.md)

## Sources

- [Why is Everyone Missing This with AI Agents?! (Memory + Tools that Scale)](./why-is-everyone-missing-this-with-ai-agents-memory-tools-that-scale.md) - "[0:04:52] Just-in-time authorization demonstrated: Asana auth is only requested the moment the agent first needs the Asana tool"
