Meeting Preparation Agent

The work it takes off your plate

Preparing for meetings is often resource-intensive and inefficient. Team members must manually navigate cluttered inboxes and various platforms to gather necessary details and attendee backgrounds, a process requiring significant manual effort. Synthesizing past conversations adds complexity, particularly when information is dispersed across multiple communications. This inefficiency increases the risk of missing crucial details, causing preparation delays and potential miscommunication. meeting preparation agent simplifies the meeting research and preparation by automating information aggregation and organization. Using AI to extract, synthesize, and organize data from diverse sources into actionable insights, this agent ensures comprehensive preparation. This automation minimizes the chance of overlooking important details and maximizes meeting efficiency, ensuring all participants are well-prepared and facilitating more effective and engaging interactions.

How it works

meeting preparation agent is designed to automate the research required for meetings by gathering and organizing relevant information. Using an LLM, it summarizes the professional background and key information about the attendee and generates a comprehensive meeting research report. Below, we outline the detailed steps that showcase the agent's workflow, from the agent activation to attendee information retrieval and meeting report generation.

1. Step 1: Agent Activation and Initial Data Extraction

   • The agent monitors connected calendars for newly created or updated meetings.
   • It identifies meeting participants, organizer, date, time, title, agenda, and organization domains.
   • It distinguishes internal attendees from external participants using directory and domain records.
   • It applies access controls and records the source systems used for each extracted field.

   Result: A validated meeting record is created with the external attendees and research scope clearly identified.

2. Step 2: Domain Name Analysis and Profile Search

   • The agent analyzes external attendee email domains to identify their associated companies.
   • It searches approved company, directory, and professional-profile sources for current roles and background information.
   • It extracts recent publications, speaking engagements, strategic priorities, and relevant expertise.
   • It reconciles conflicting profile details and assigns source links and confidence indicators to findings.

   Result: Each external attendee receives a structured professional profile with current, source-backed context.

3. Step 3: Previous Conversations Retrieval and Processing

   • The agent retrieves permitted email threads, meeting notes, chat messages, and shared documents involving the attendees.
   • It filters conversation history by relevance, recency, organization, and meeting topic.
   • It summarizes prior commitments, unresolved questions, expressed interests, and potential risks.
   • It separates confirmed facts from inferred context and preserves links to the supporting conversations.

   Result: A concise conversation brief connects historical interactions to the objectives of the upcoming meeting.

4. Step 4: Meeting Report Generation

   • The agent combines meeting details, attendee profiles, conversation insights, and source citations into a preparation report.
   • It proposes discussion topics, focused questions, risks, and recommended preparation actions.
   • It highlights missing information, time-sensitive developments, and follow-up opportunities for the organizer.
   • It delivers the report through the configured collaboration or workflow channel and logs the delivery status.

   Result: Participants receive an actionable, source-linked meeting brief that supports focused discussions and informed follow-up.

Why use it

• Enhanced Productivity. Automates tedious meeting preparation tasks, allowing users to focus on strategic activities rather than spending time gathering and organizing information.
• Error Reduction. Minimizes the risk of missing crucial details or misinterpreting information by using LLMs for data extraction, analysis, and summarization.
• Scalable and Adaptable. Easily scales to handle multiple meetings across different teams or projects and adapts to various organizational workflows and requirements.
• Contextual Insights. Uses past communication history and attendee profiles to provide context for upcoming meetings, enabling continuity and effective decision-making.
• Time Savings. Reduces the time spent searching for attendee details, summarizing previous conversations, and compiling meeting agendas, significantly improving efficiency.