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Internal AI Operating System

Build an internal company OS where AI agents handle tasks, manage memory, route work, and automate business processes.

Best for: Companies wanting to embed AI into daily operations

What You Get

  • -Agent task queue and scheduling
  • -Persistent agent memory (Squish)
  • -Role-based agent delegation
  • -Workflow automation engine
  • -Dashboard for monitoring

Step by Step

1. Define agent roles and permissions

Create an agent registry with roles: researcher (read/analyze), writer (create/edit), data-agent (query/manage data), ops-agent (execute actions). Each role has allowed tools and permission levels.

2. Build the task queue

Create a PostgreSQL-backed task queue. Fields: id, title, description, assigned_agent, priority (low/medium/high/urgent), status (pending/in_progress/done/failed), source (human/agent), created_at, completed_at.

3. Implement agent execution engine

Each agent is an LLM call wrapped with: task context, role instructions, allowed tools, and Squish memory access. Agents use ReAct loop: think, act, observe, repeat until task completion.

4. Add Squish memory integration

Configure Squish MCP server per agent. Agents automatically remember: completed tasks, user preferences, key decisions, and frequently used data. Memory is searchable across sessions.

5. Build workflow automation

Create a workflow engine: when task A is completed with status 'done', check workflow rules and trigger task B. Rules are defined as JSON: trigger_task, condition, target_agent, priority.

6. Implement human-in-the-loop

Flag sensitive actions (email sending, data deletion, payment actions) for human approval. Create an approval queue in the dashboard. Agents pause and wait for approval before proceeding.

7. Build the dashboard

Next.js dashboard showing: agent status (online/offline/busy), task queue with filters, memory stats (memories per agent, storage used), workflow runs history, and audit log.

Stack

Squish (memory)OpenClaw (agent orchestration)Neon PostgresNext.jsOpenAI/Claude

Build This

Copy this prompt and paste it into Claude Code, OpenCode, Codex, or Cursor to build this recipe.

Build me an internal AI operating system for a company. It should: 1) Support multiple AI agents with different roles (research agent, writing agent, data agent, ops agent). 2) Include a task queue where humans can assign tasks to specific agents with priority levels. 3) Give each agent persistent memory using Squish so they remember context across sessions. 4) Support workflow automation: when task A completes, trigger task B with the output. 5) Include a dashboard showing agent status, recent tasks, and memory usage. 6) Add a human-in-the-loop approval step for sensitive actions (sending emails, making changes). 7) Log all agent actions for audit. The system should run on a single VPS with Neon Postgres as the database.

Common Failure Modes

  • !Agent hallucinations from bad memory retrieval
  • !Task queue bottlenecks
  • !Permission escalation risks
  • !Cost from constant LLM calls

Implementation Notes

Start with 2-3 agents. Monitor LLM costs closely. Implement rate limiting per agent. Audit log all agent actions.

Want internal ai operating system running in your business?

4M Labs can deploy internal ai operating system as a production workflow:

  • Connected to your tools and data sources
  • Secured for your team with proper access controls
  • Deployed with monitoring and error handling
  • Documented for handoff and future maintenance
Book an Implementation Sprint