Clean your tools. Then turn what’s left into an AI-powered operating system.
Most companies try to “add AI” on top of a messy stack.
That’s like installing a self-driving engine in a car with 3 steering wheels.
This prompt does the correct order:
Step 1 → Identifies Unused Tools
Step 2 → Designs an Agentic AI system that runs the business
*For a business just staring out we suggest doing step 1, evaluating the results and then proceeding to step 2 once you have a better grasp on how AI works.
Phase 1 — Stack Cleanup
✔ Find overlapping tools
✔ Flag underused platforms
✔ Identify integration bottlenecks
✔ Estimate cost leakage
✔ Receive recommended Keep / Replace / Remove guidance for specific AI software.
Phase 2 — Agentic AI System Design
✔ Convert your cleaned workflows into a multi-agent architecture
✔ Assign tools to specific AI agents
✔ Define AI vs human decision boundaries
✔ Build orchestration logic + fallbacks
✔ Output an execution-ready AI operating model
You don’t just reduce tools.
You turn the remaining stack into a coordinated AI system.
Before we dive into prompts, here’s the flow in plain English. You’ll give ChatGPT your key inputs once.
Have These Ready:
Struggling? Here’s the Sample Data We Used:
👉 Copy this prompt into ChatGPT, fill in the {{insert}} placeholders, then run it.
You are a Systems Architect and Agentic AI Designer.
Input:
- Tools: {{list of tools}}
- Tool Purpose: {{what each tool is supposed to do}}
- Core Workflows: {{how the business operates}}
- Friction Points: {{where inefficiencies exist}}
PHASE 1 — STACK AUDIT
1) Identify overlapping or redundant tools.
2) Flag tools likely underused.
3) Detect workflow bottlenecks caused by tool switching.
4) Estimate cost leakage.
5) Output a Keep / Replace / Remove list with risk notes.
PHASE 2 — AGENTIC AI SYSTEM DESIGN
6) Convert cleaned workflows into a multi-agent architecture.
7) Define agent roles (planner, execution, data, monitoring, human).
8) Assign tools to agents with clear responsibilities.
9) Design orchestration logic and fallback rules.
10) Output:
• Simplified stack model
• Agent role map
• AI vs Human boundaries
• Execution-ready system blueprint
You’ll end up with the following:
✔ Lean, efficient tech stack
✔ Clear tool ownership
✔ Fewer integrations to break
✔ AI agents working across the stack
✔ A true AI operating system, not scattered automations
Most AI advice tells you what to add.
Sign up for a “free trial”, book a “demo!”
This framework starts by deciding what to remove.
Instead of layering intelligence onto a broken foundation, it fixes the system first — then designs AI around how the business actually runs. It connects tools, workflows, and decision-making into one coordinated structure rather than scattered automations.
AI doesn’t create leverage on its own real structure does.
The companies that win won’t be the ones with the most tools, but the ones whose systems think and act together.
When your stack is simplified and your workflows are mapped, AI stops being an experiment and starts becoming infrastructure.
That’s when efficiency compounds, decisions get faster, and teams stop fighting their tools. Build the system, then let AI scale it.
Want to see how we build AI infrastructure for companies first hand? Book a strategy session below to dive deeper.
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