iZenBridge Consultancy Pvt Ltd.

In this session, we explore how an AI agent can support backlog intake and refinement using Make.com, Google Forms, Google Sheets, Google Docs, and AI Web Search.

Instead of simply asking AI to “write a user story,” this demo shows how an agent can receive stakeholder feedback, research external signals, check product context, review existing backlog items, look at past decisions, and then recommend the right backlog action.

In the demo, the agent evaluates whether a request should become a new story, update an existing backlog item, be clarified, split, rejected, or kept pending. We also discuss how to test agent behavior using test cases and how to estimate the cost of agent execution using Make credits.

What we cover - What makes a workflow “agentic” - Difference between automation, chatbot, and AI agent - Backlog intake challenge in product/project work - Make.com workflow for agentic backlog intake - Use of AI Web Search for competitor and community signals - Reading product knowledge, backlog, and past decisions - Creating structured intake recommendations - Testing AI agent behavior with test cases - Estimating Make.com credit consumption per agent run

This session is useful for project managers, product owners, scrum masters, agile coaches, business analysts, and anyone exploring practical AI agent use cases in project and product management.

Key idea: AI agents are not just about automation. They need goals, tools, memory, reasoning, governance, testing, and cost control.

#AIAgents #Makecom #ProjectManagement #ProductManagement #BacklogRefinement #GenerativeAI #AIforProjectManagers #Agile #Scrum #Automation