Most teams rush to build AI agents before asking the most important question: ❓ Should you even build one? Microsoft's Cloud Adoption Framework has a decision tree that cuts through the noise. Here's what it actually says: 🔷 Step 1 — Business Plan Check If your task is structured or predictable → skip agents entirely. Use GitHub, Microsoft Fabric, or ML models instead. If it's static knowledge retrieval → build a RAG app, not an agent. 🔷 Step 2 — SaaS Before Custom Before writing a single line of custom agent code, ask: does M365 Copilot, GitHub Copilot, Azure Copilot, or Dynamics 365 already solve this? If yes → use it. If no → now you can build. 🔷 Step 3 — Architecture Decision Single agent or multi-agent? Only go multi-agent if you're crossing security/compliance boundaries, involving multiple teams, or planning for serious scale. Otherwise? Test a single agent first. If it passes → ship it. If it fails → then escalate to multi-agent. The framework's core philosophy: 👉 Don't build what already exists. 👉 Don't over-engineer what can stay simple. 👉 Let the use case — not the hype — drive the architecture. This is the kind of structured thinking that separates mature AI adoption from "we just used agents because everyone else is." 💬 Where does your team usually get stuck in this decision process? Would love to hear in the comments. Follow @AiswaryaVenkit1 for more AI insights.