aitbc/docs/apps/agents/ai-engine.md

AI Engine

Status

✅ Operational

Overview

AI engine for autonomous agent operations, decision making, and learning capabilities.

Architecture

Core Components

• Decision Engine: AI-powered decision making module
• Learning System: Real-time learning and adaptation
• Model Management: Model deployment and versioning
• Inference Engine: High-performance inference for AI models
• Task Scheduler: AI-driven task scheduling and optimization

Quick Start (End Users)

Prerequisites

• Python 3.13+
• GPU support (optional for accelerated inference)
• AI model files

Installation

cd /opt/aitbc/apps/ai-engine
.venv/bin/pip install -r requirements.txt

Configuration

Set environment variables in .env:

AI_MODEL_PATH=/path/to/models
INFERENCE_DEVICE=cpu|cuda
MAX_CONCURRENT_TASKS=10
LEARNING_ENABLED=true

Running the Service

.venv/bin/python main.py

Developer Guide

Development Setup

1. Clone the repository
2. Create virtual environment: python -m venv .venv
3. Install dependencies: pip install -r requirements.txt
4. Download or train AI models
5. Configure model paths
6. Run tests: pytest tests/

Project Structure

ai-engine/
├── src/
│   ├── decision_engine/     # Decision making logic
│   ├── learning_system/     # Learning and adaptation
│   ├── model_management/    # Model deployment
│   ├── inference_engine/    # Inference service
│   └── task_scheduler/      # AI-driven scheduling
├── models/                  # AI model files
├── tests/                   # Test suite
└── pyproject.toml           # Project configuration

Testing

# Run all tests
pytest tests/

# Run specific test
pytest tests/test_inference.py

# Run with GPU support
CUDA_VISIBLE_DEVICES=0 pytest tests/

API Reference

Decision Making

Make Decision

POST /api/v1/ai/decision
Content-Type: application/json

{
  "context": {},
  "options": ["option1", "option2"],
  "constraints": {}
}

Get Decision History

GET /api/v1/ai/decisions?limit=10

Learning

Trigger Learning

POST /api/v1/ai/learning/train
Content-Type: application/json

{
  "data_source": "string",
  "epochs": 100,
  "batch_size": 32
}

Get Learning Status

GET /api/v1/ai/learning/status

Inference

Run Inference

POST /api/v1/ai/inference
Content-Type: application/json

{
  "model": "string",
  "input": {},
  "parameters": {}
}

Batch Inference

POST /api/v1/ai/inference/batch
Content-Type: application/json

{
  "model": "string",
  "inputs": [{}],
  "parameters": {}
}

Configuration

Environment Variables

• AI_MODEL_PATH: Path to AI model files
• INFERENCE_DEVICE: Device for inference (cpu/cuda)
• MAX_CONCURRENT_TASKS: Maximum concurrent inference tasks
• LEARNING_ENABLED: Enable/disable learning system
• LEARNING_RATE: Learning rate for training
• BATCH_SIZE: Batch size for inference
• MODEL_CACHE_SIZE: Cache size for loaded models

Model Management

• Model Versioning: Track model versions and deployments
• Model Cache: Cache loaded models for faster inference
• Model Auto-scaling: Scale inference based on load

Troubleshooting

Model loading failed: Check model path and file integrity.

Inference slow: Verify GPU availability and batch size settings.

Learning not progressing: Check learning rate and data quality.

Out of memory errors: Reduce batch size or model size.

Security Notes

• Validate all inference inputs
• Sanitize model outputs
• Monitor for adversarial attacks
• Regularly update AI models
• Implement rate limiting for inference endpoints