AI-Agent Orchestrator

Seamlessly connect, deploy, and manage multi-agent AI workflows in record time.

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Inspiration

The inspiration for this project came from a simple question:
What if a user could generate a complete machine learning project just by describing their idea?

Most ML workflows require repetitive manual steps like data preparation, model selection, training, and API deployment. I wanted to automate this entire pipeline using multiple collaborating AI agents. These help people with non tech background to create a lot of projects on their own.

What it does

I built a Multi-Agent AI Orchestrator System that takes a single user prompt and automatically generates a complete machine learning project.

The system consists of specialized agents:

  • Requirements Agent → understands and structures the problem
  • Planning Agent → designs ML architecture and workflow
  • Code Agent → generates full working Python ML project
  • Dataset Agent → creates dataset schema and sample data
  • Documentation Agent → generates professional README documentation

All agents are powered by Groq LLM (Llama 3.1)for fast and reliable execution.

How we built it

The system is built in Python using a modular agent-based architecture.

Each agent performs a specific task and passes structured output to the next stage.

The pipeline works as follows:

[ \text{User Prompt} \rightarrow \text{Requirements Agent} \rightarrow \text{Planning Agent} \rightarrow \text{Code Agent} \rightarrow \text{Dataset Agent} \rightarrow \text{Documentation Agent} ]

Finally, all outputs are saved into a fully runnable ML project inside the output_project/ folder.

Challenges we ran into

Some major challenges I faced include:

  • Handling API rate limits and failures from LLM providers especially with gemini-2.0-flash. I used gemini-2.0-flash,gemini-2.0-pro,gemini-1.5-flash etc but due to its limited count I was not able to continue further.
  • Designing a stable fallback system when APIs fail
  • Parsing structured outputs from LLMs reliably
  • Ensuring consistent file generation across multiple agents -A lot of trial and error in different agents. -It was difficult to generate files but finally it was a success. The project generates files, instructions, code inside project_output.

I solved these by simplifying the system and making Groq the primary execution engine for stability.

Accomplishments that we're proud of

-Completing a hackathon on my own. -Finding a problem and fully implementing the solution and that too in a fully working environment. -Help a lot of people to generate projects based on the same.

What we learned

The final system can generate:

  • Complete ML training pipeline
  • Prediction scripts
  • Flask API for deployment
  • Dataset generation
  • Documentation

from just a single user prompt.

What's next for AI-Agent Orchestrator

In the future, I plan to:

  • Add memory-based agents
  • Introduce LangGraph orchestration
  • Add UI dashboard for interaction
  • Support OpenAI + Gemini + Groq dynamic routing -And a lot more.

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