Summary  
This chapter introduces three programming approaches for building AI-driven decision systems—prompt-only reasoning, tool-grounded data integration, and reusable agent-based workflows—to ensure context-aware, reliable outcomes.  

General domain of usage  
Business decision support

This lab assumes basic familiarity with modern AI tools and prompting techniques.
If you'd like to improve the quality, consistency, and reliability of your AI workflows, we recommend starting with our **Practical AI for Work**.

Your manager pings you Tuesday afternoon:
> <b>Do you think we should switch from Slack to Teams? Finance keeps pointing out we're already paying for Teams through the Microsoft license.

You open Claude and ask. You get back a clean, confident-sounding case for switching. Problem is, it's working off nothing but the one sentence you typed. It doesn't know what your Slack plan actually costs, whether Teams would really cover everything the team uses Slack for, or that a partial migration got tried last year and stalled out. That's an opinion, and you're about to hand it to your manager like it's an analysis.



## Three Ways to Ask the Same Question

This course tackles one real decision per section and solves it three times, each time with more to work with:

* **Prompt only**: Claude reasons from just the context you give it. Fast, but only as good as what you typed;
* **Tool-grounded**: Claude checks something real before answering: current prices, your actual numbers, what was decided last time;
* **Agent**: the whole approach gets packaged into something reusable, so next time your manager pings you with a similar call to make, you're not starting over.



## Why This Matters More for Decisions Than Anything Else

A vague answer to an explanation question just wastes a few minutes. A vague answer to "should we switch tools" can talk you into recommending a switch that costs real money and real migration pain, on the strength of a confident paragraph that never checked a single real number. **That's the whole point of building an actual decision system instead of just asking for an opinion.**

Master the skills needed to build reliable AI decision systems with Claude, tackling real business calls like switching tools, approving a hire, and renewing a vendor contract. Progress from prompt-only reasoning to tool-grounded analysis with live data, then package your approach into a reusable Claude agent with structured output and a no-invented-facts constraint. By the end, you'll write a full agent system prompt from scratch.

Discover how to build an AI decision system for a real tool-switch call. Start with prompt-only reasoning, ground it in live pricing and migration data via web search, then build a Tool Switch Advisor agent that routes tasks and cites its sources.

Explore the essentials of contextual prompting for hiring decisions. Compare a bare prompt to a context-rich one, analyze real team capacity data to test the case for a hire, then build a Hiring Decision Advisor agent around that data.

Dive into the fundamentals of building a Claude agent for a vendor renewal decision. Combine past notes, usage data, and market research into one recommendation, then write your own agent prompt with tool routing and a no-invented-facts rule.

What Makes an AI Decision System?

Three Ways to Ask the Same Question

Why This Matters More for Decisions Than Anything Else