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How to automate meeting notes and follow-ups with an AI agent
I looked through 106 real cases about meeting notes and follow-ups and pulled out what people actually did, the prompts they used and the places it went sideways. No theory, no invented success stories.
What people actually do
- Coordinate a cancer patient's records and care
- Claude Code's 3-word status line reportedly burns 15 to 20% of tokens
- Chase an airline refund dispute
- Summarize meetings and triage support tickets
- tl;dv boosts revenue 500% from AI-powered meeting intelligence with Claude
- Zoom AI Companion boosts user engagement and satisfaction with Claude
- Vambe powers conversational commerce across Latin America with Claude
- Providers suffer from data overload. Agentforce powers Takeda’s digital solution for healthcare engagement.
Browse all 2754 cases to see the rest.
Prompts to start from
These are the reusable prompts from real cases. Swap in your own details, run it once on something low-stakes, and read what the agent did before you trust it.
Home/family task: coordinate a cancer patient's records and care. Keep changes reversible and ask before messages, payments, or deletes.
Help me find out where my coding agent's tokens actually go. Look at my usage logs or the tool's own cost report and break requests down by purpose: main conversation, background agents, summaries, status or title generation, and retries. Show each as a share of total requests and total spend. Flag anything that runs on a timer rather than when I ask, estimate what it costs me per week, and tell me whether the tool has a setting to turn it off or whether I need a workaround. Say clearly which numbers are measured and which are your estimates.
Tools that show up most
Custom agent (19), Claude (13), Claude Code (8), ChatGPT (6), Lindy (5), Agentforce (5).
Guardrails
Start small: run it once on a low-stakes example, read what the agent did, and only then let it run on real work.
More mess: the AI agent fails page. Related: the prompt library.