AI Agent Generator

A PyTorch-based framework to automatically generate AI agents tailored to user needs. Provide a simple natural-language prompt, and the system constructs a transformer-based agent (with embeddings, attention, and slot-filling) ready to be deployed as a conversational assistant for industries, customer support, education, and more. (work in progress, starting with the transformers, have not built the agent workflow generating)

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Table of Contents

• Overview
• Features
• Architecture
• Getting Started
  • Prerequisites
  • Installation
• Usage
  • Generating a New Agent
  • Customizing Slots & Intents
• Data & Training
• Project Structure
• Contributing
• License
• References

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Overview

This project lets you turn a one-line prompt (e.g., “Build a customer-support agent for the educational sector”) into a fully operational transformer-based AI agent. The pipeline:

1. Prompt Parsing: Extract high-level intent and required details (industry, domain, slots).
2. Agent Generation: Auto-assemble embeddings, encoder–decoder layers, and slot classifiers.
3. Deployment Ready: Export the trained model and inference loop for integration into chat applications.

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Features

• Prompt-Driven Agent Creation
  Build a specialized chatbot by simply describing your use case.

• Rich Embeddings
  Token + role + turn-index embeddings with scaling, normalization, and dropout.

• Dynamic Slot-Filling
  Configurable slots (industry, urgency, product, etc.) extracted via classification and span prediction.

• Prefix-Caching Decoder
  Efficient incremental generation of clarifying questions and final responses.

• Modular Design
  Swap in new attention heads, embeddings, or slot schemas with minimal changes.

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Architecture

1. Prompt Parser

   • CLI or API to parse user prompt into intent + slot schema.

2. Agent Builder

   • Programmatically instantiate TransformerChatbot with slot definitions.

3. Embedding Layer

   • RichEmbeddings: tokens + roles + turns.

4. Transformer Core

   • Encoder–decoder stacks with multi-head attention and feed-forward networks.

5. Slot Classifier & Extractor

   • Heads to mark slots as filled/missing and retrieve values.

6. Inference Engine

   • Dialogue manager to run multi-turn Q&A, fill slots, and finalize agent.

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Getting Started

Prerequisites

• Python 3.7+
• PyTorch 1.7+
• NumPy

Installation

git clone https://github.com/yourusername/ai-agent-generator.git
cd ai-agent-generator
python -m venv .venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows
pip install -r requirements.txt

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Usage

Generating a New Agent

# Example: create an agent for educational customer support
python run_agent_generator.py \
  --prompt "Build an AI agent for answering student questions in the educational sector" \
  --output_dir ./agents/education_bot

This produces:

• model.pth: trained weights
• config.json: slot definitions and hyperparameters
• inference.py: script to run the chat interface

Customizing Slots & Intents

Pass a JSON schema to define custom slots:

{
  "slots": ["industry", "department", "urgency"],
  "intents": ["support", "sales", "feedback"]
}

Use --schema my_schema.json when running run_agent_generator.py.

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Data & Training

1. Prepare Dialogues: Multi-turn conversations with slot annotations.
2. Tokenize & Tag: Generate token, role, and turn tensors.
3. Train: python train.py --data data/dialogues.json
4. Evaluate: Slot accuracy, dialogue length, and response quality.

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Project Structure

ai-agent-generator/
├── src/
│   ├── embeddings.py         # RichEmbeddings
│   ├── positional_encoding.py
│   ├── attention.py          # MultiHeadAttention & ScaledDotProduct
│   ├── transformer.py        # TransformerChatbot
│   ├── slot_classifier.py
│   ├── run_agent_generator.py
│   └── train.py
├── agents/                   # Saved generated agents
├── data/                     # Sample dialogue datasets
├── requirements.txt
└── README.md

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Contributing

1. Fork the repo
2. Create a branch (git checkout -b feature/new-agent)
3. Commit your changes
4. Push to your fork and open a PR

Please add tests and update documentation for new features.

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License

MIT License

Feel free to adapt and extend this framework to generate AI agents for any domain!

References & Further Reading

• Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
• “The Annotated Transformer” by Harvard NLP
• “The Illustrated Transformer” by Jay Alammar
• PyTorch’s official Transformer tutorial