Reasoning Engine

⚡ 1-Minute Summary Instructions resolve top to bottom. Logic lines run. Prompt lines become English. The model never sees your script. It sees only the finished prompt your logic built. Shorter reasoning instructions produce more accurate agents. Verbosity is not thoroughness. Module map MODULE 2 root: 'What actually happens in one turn?' ├─ 2.1 Anatomy of one turn ├─ 2.2 How the prompt gets built └─ 2.3 Why shorter instructions win Anatomy of One Turn 💬 In plain words: Every message follows the same seven steps: router, before-reasoning, instructions, model, tools, after-reasoning, reply. Learn this sequence and every design decision in Agentforce explains itself. 📌 Example: A Meridian customer says "is my power back yet?". The router picks the outage subagent. Before-reasoning verifies their meter. Instructions run a status action and branch. The model phrases the answ...

⚡ 1-Minute Summary An agent understands language, decides what to do, and acts. A chatbot only answers. Every example in this notebook uses one company: Meridian Power. Same company, every module. Learn the 360 Card first (0.5). It is the shape of every good answer: rule, gain, price, limits. Module map MODULE 0 root: 'What is Agentforce, and how do the modules fit together?' ├─ 0.1 The map: one agent, six layers ├─ 0.2 Meridian Power — the example company ├─ 0.3 Key terms in plain words ├─ 0.4 The rename map (2024-2026) └─ 0.5 The 360 Card — how to answer any question 0.1 The Map: One Agent, Six Layers 💬 In plain words: Agentforce looks like many products. It is not. It is one agent with six layers stacked under it. Each layer answers one question, and each module of this notebook lives on one layer. 📌 Example: A customer types "my power is out." Identity decides whose dat...