Empowering product development with an agentic workflow

Product development teams face constant pressure to move quickly while maintaining alignment across stakeholders. Traditional methods of converting stakeholder discussions into actionable development plans often involve manual, time-consuming processes that can introduce errors and delays. By using AI Agents, teams can dramatically accelerate this workflow while improving accuracy and consistency.

Figure 1: A high-level workflow

Eliminating bottlenecks in manual PRD and engineering task creation

The journey from initial product discussions to actual development typically involves multiple manual steps: 

1. Transcribing meetings.

2. Drafting Product Requirements Documents (PRDs)

3. Creating individual engineering tickets

Product managers often spend hours converting raw meeting notes into structured documentation, and engineers waste valuable time interpreting requirements and breaking them down into actionable tasks. This process can create bottlenecks, especially as organizations scale and the volume of product initiatives increases.

But what if we could automate this entire workflow? What if we could take a meeting transcript and automatically generate both a comprehensive PRD and a set of actionable development tickets? This would not only save time but ensure consistent documentation and improved alignment across teams.

Automating PRD and engineering ticket generation with AI agents

Consider a typical product planning cycle. Stakeholders meet to discuss new features, but the follow-up work of documentation and task creation consumes valuable time that could be spent on actual development. By implementing an automated agentic workflow, teams can dramatically reduce this overhead.

Building an efficient agentic workflow with Mistral AI

Figure 2: TranscriptToPRDTicket agentic workflow

At its core, our agentic solution leverages two key components: PRDAgent and TicketCreationAgent, both powered by Mistral Large 2, a state-of-the-art LLM. The workflow follows a clear progression: