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PowerBI Builder

Inspiration

Creating professional Power BI dashboards is a time-consuming process that usually requires expertise in data modeling, DAX, visualization design, and business analysis. I wanted to build an AI system that could automate this workflow while still producing explainable and reliable results.

What it does

PowerBI Builder is an AI-powered multi-agent system that converts Excel, CSV, or JSON datasets together with natural language instructions into complete Power BI (.pbip) projects.

The generated project includes:

Semantic models
Relationships
DAX measures
Dashboard layouts
Business insights
Documentation
Explainability logs

Instead of generating a single solution, the system explores multiple candidates, evaluates them, selects the best one, validates the result, and produces a deployable Power BI project.

How I built it

The project combines Google's Agent Development Kit (ADK) with a deterministic multi-agent generation engine.

Specialized agents collaborate throughout the pipeline, including:

Planner Agent
BI Reasoning Agent
Data Analyzer Agent
Data Cleaner Agent
Schema Agent
Relationship Agent
DAX Agent
Visual Planner Agent
Report Agent
Validator Agent
Judge Layer

Google ADK manages agent orchestration, tool execution, and user interaction, while the deterministic engine ensures reproducible and explainable outputs.

Challenges I ran into

One of the biggest challenges was balancing AI flexibility with deterministic and reproducible results.

Other challenges included:

Building a modular multi-agent architecture
Designing candidate-based reasoning
Validating generated semantic models
Producing consistent DAX measures
Coordinating communication between multiple agents
Keeping the generated Power BI project reliable and explainable

Accomplishments that I'm proud of

Built an end-to-end AI system for Power BI generation
Implemented multi-agent orchestration using Google ADK
Added candidate-based decision making
Implemented explainable AI through decision logs
Generated complete PBIP projects automatically
Created an extensible architecture for future editing and optimization

What I learned

This project reinforced how effective agentic systems become when reasoning is combined with deterministic engineering.

I also gained practical experience designing scalable multi-agent workflows, validation pipelines, explainability mechanisms, and AI-assisted BI automation.