Inspiration

In many organizations, tasks get delayed because of missing documents, pending approvals, communication gaps, and missed deadlines. These problems can slow down the entire workflow.

We wanted to create a solution that helps teams identify workflow problems early and find ways to resolve them.

What it does

AgentFlow AI is a workflow monitoring and bottleneck detection prototype that helps users:

Monitor tasks and their progress.
Identify blocked and overdue tasks.
Detect possible reasons for workflow delays.
Generate follow-up message drafts.
Suggest recovery actions.
Verify recovery conditions.
Maintain an audit trail of actions.

How we built it

We built the prototype using Python and Streamlit. We used Pandas for handling task data, NetworkX for workflow dependencies, and Plotly for visualizing workflow information.

The prototype uses workflow rules to identify bottlenecks and simulate recovery actions. It provides a simple dashboard to monitor and manage tasks.

Challenges we faced

As beginners, we faced challenges in setting up the development environment, understanding workflow dependencies, handling task statuses, and implementing recovery verification.

We solved these challenges step by step by testing the application and improving its functionality.

What we learned

We learned how to build a Python-based web application, monitor workflow tasks, identify bottlenecks, simulate automated actions, and maintain an audit trail.

Future scope

In the future, we aim to integrate machine learning for smarter bottleneck prediction, real-time notifications, email integration, and connections with business management systems.