Results for 'slack pricing vs microsoft teams cost comparison':\n\n1. Slack pricing page — Business+ plan: $12.50/user/month billed annually, includes SAML SSO, compliance exports, and a 99.99% uptime SLA. Pro plan: $7.25/user/month, lacks SSO and compliance exports. Source: slack.com/pricing.\n\n2. Microsoft 365 pricing page — Teams is bundled at no extra cost with Business Standard ($12.50/user/month) and E3 ($36/user/month) licenses. If a company already holds either license, the incremental cost of using Teams is $0.\n\n3. G2 comparison, 'Slack vs Microsoft Teams' — reviewers consistently cite Teams' notification and threading model as a real adjustment for teams coming from Slack; 'ease of adoption after switching' rates notably lower for Slack-to-Teams migrations than the reverse.\n\n4. Reddit r/sysadmin thread, 'We switched from Slack to Teams to save money, AMA' — top comment: license savings were real, but rebuilding custom Slack bots and integrations on Teams took the IT team roughly six weeks, eating most of the first year's savings.\n\nAuto-detected: across sources, the recurring theme isn't the license cost — Teams is often already paid for — it's the hidden cost of rebuilding integrations and the adjustment period for notification/threading habits.

Summary  
This chapter covers how to implement an AI agent by composing a system prompt, integrating a search tool for live data, and defining structured response sections to automate decision-making.  

General domain of usage  
AI-powered decision support

You've done this manually twice now — reasoned through it cold, then grounded it in real numbers. Time to package it so the next tool-switch decision doesn't start from scratch.

## What You're Building

An agent that always checks real pricing and reviews before answering, always argues the case against its own recommendation, and always flags what it doesn't know about your specific situation.

A decision advisor, not a generic assistant

Always check current pricing and reviews, never answer from memory, since prices change

Recommendation, case against it, open unknowns

Never state a price or fact unless the search actually returned it

Combined into one system prompt:
> <b>You are a tool-switch decision advisor. When the user asks whether to switch between two work tools, use the web_search tool to check current pricing, features, and real-world migration experiences before answering — never rely on memory for costs, since pricing changes. Always return exactly three sections: Recommendation (one clear call: switch, stay, or something else), Strongest Case Against It (argue against your own recommendation using what the search actually found), Open Unknowns (anything about the user's specific situation that would change the answer, which the search can't tell you). Never state a price, feature, or migration account as fact unless the search actually returned it.

> ## Task
> - Click "Create Agent".
> - Agent name:
>   > <b>Tool Switch Advisor
> - System prompt → paste the block above
> - Choose tools → check Web Search
> - Save, then activate the agent for this chapter
> - Send: 
>     ><b>Should we switch from Slack to Teams? We're a 40-person team on Slack's Business+ plan, already licensed for Microsoft 365, and a partial migration attempt stalled last year.
>
> Check your result: did it cite the actual $12.50 price point and the real integration-rebuild cost from the search, or just repeat generic tradeoffs?

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.