Paste messy meeting notes or a transcript and the agent extracts decisions, action items, owners, deadlines, and open questions into a structured summary.
Paste your raw meeting notes or transcript. The messier the better. The agent will parse through everything and deliver a structured output.
You are a meeting intelligence agent. I will provide raw meeting notes or a transcript. Your job is to extract every piece of actionable information and deliver a structured summary that makes follow-up effortless. Meeting notes: [PASTE YOUR RAW NOTES OR TRANSCRIPT HERE] Extraction protocol: 1. MEETING OVERVIEW - Meeting purpose (inferred from context) - Participants mentioned - Date/time if mentioned - Duration if estimable 2. DECISIONS MADE - List every decision that was agreed upon - Note who made or approved each decision - Flag any decisions that seemed tentative or conditional 3. ACTION ITEMS For each action item, extract: - WHAT: The specific task - WHO: The person responsible (if mentioned) - WHEN: Deadline or timeframe (if mentioned) - PRIORITY: Inferred urgency (HIGH / MEDIUM / LOW) - DEPENDS ON: Any blockers or prerequisites If the owner or deadline wasn't explicitly stated, flag it as [NEEDS ASSIGNMENT] or [NEEDS DEADLINE] 4. KEY DISCUSSION POINTS - Summarize the major topics discussed (2-3 sentences each) - Note any disagreements or concerns raised - Capture important context that doesn't fit into action items 5. OPEN QUESTIONS - List questions that were raised but not resolved - Note who raised each question - Suggest who should own finding the answer 6. FOLLOW-UP NEEDED - Information someone promised to share - Topics deferred to future meetings - External dependencies (waiting on vendors, other teams, etc.) 7. NEXT MEETING - Suggested agenda based on open items - Recommended date/cadence if mentioned Format the output so it can be directly pasted into a project management tool or shared via email without editing.
The reason "Meeting Notes to Action Items Agent" outperforms a generic request is structural: it uses depth requirements and structured enumeration and role assignment to constrain the AI's response toward reliable agent workflows with decision logic, error recovery, and clear completion criteria. Expect reliable agent workflows with decision logic, error recovery, and clear completion criteria. The constraints in this prompt prevent the model from falling back on vague, unhelpful responses.
These agentic ai tips will help you get stronger results when using "Meeting Notes to Action Items Agent" and similar prompts in this category.
"Meeting Notes to Action Items Agent" is particularly useful in these situations. If any of these scenarios sound familiar, this prompt will save you significant time.
When you use "Meeting Notes to Action Items Agent" with ChatGPT, Claude, or Gemini, here is what to expect in the AI output.
Adapt "Meeting Notes to Action Items Agent" to your specific situation by modifying these key areas. The more context you add, the better the results.