Add multi-agent AI/ML platform: AGI, MLOps, DataOps, DevOps, NLU/NLP, Neural Networks, Human Connection

[Copilot]

Extends the repository with a full multi-agent AI/ML platform built entirely on Python standard library (no PyTorch/TensorFlow dependencies). 121 new source files across 10 major module trees.

New Modules

• agents/agi_agent/ — Contextual reasoning engine, knowledge graph, meta-learner, transfer learning, multi-objective decision optimizer
• agents/nlu_agent/ — Intent classifier, NER entity extractor, conversation context manager, sentiment analyzer, multi-turn dialogue manager
• agents/nlp_agent/ — Text generator, document summarizer, translator, QA system, technical doc parser
• mlops/ — Model registry (versioning + metadata), training pipeline, feature store, performance monitor, A/B testing, drift detector, auto-retrain triggers
• dataops/ — Ingestion (connectors, streaming, batch), quality (validators, cleansing, profiler), lineage (tracker, visualizer), catalog (metadata store, search)
• devops/ — CI/CD (pipeline generator, test runner, deployment), IaC (Terraform/Ansible/CloudFormation adapters), observability (logging/metrics/tracing), secrets (Vault + key manager)
• neural_networks/ — Transformer simulations (BERT/GPT/T5), GNN for infra topology, LSTM time-series forecasting, CNN log analysis, RL policy optimizer + resource allocator
• human_connection/ — Emotional intelligence (empathy detector, tone adjuster, stress monitor, compassion engine), purpose-driven design (ethics checker, accessibility, empowerment coach), personalization, wellbeing (workload analyzer, break suggester, celebration engine)
• integration/ — Orchestration bridges connecting all modules: AGI orchestrator, ML pipeline connector, DataOps/DevOps bridges, NLU/NLP router, neural net inference server, human-centric wrapper
• config/ — YAML/JSON model configs for blast radius, cost optimizer, intent classifier models; BERT/GPT network configs; empathy patterns
• training_data/ — Directory scaffolding with READMEs for infrastructure logs, user intents, policy examples, emotional contexts

Notes

All implementations use only Python stdlib (dataclasses, typing, logging, abc, uuid, collections, etc.) — no heavy ML framework installs required to import the modules. A .gitignore is included to exclude __pycache__ artifacts.

Original prompt

Enhance Multi-Agent Platform with Advanced AI/ML Capabilities

Extend the Agentic Infrastructure Control Platform with AGI, MLOps, DataOps, DevOps, NLU, NLP, Neural Networks, and human-centric design principles.

New Components to Add

1. AGI (Artificial General Intelligence) Module

AGI Reasoning Engine (/agents/agi_agent/)

• Contextual reasoning - Multi-domain problem solving
• Transfer learning - Apply knowledge across different scenarios
• Meta-learning - Learn how to learn from limited examples
• Adaptive decision making - Evolve strategies based on outcomes
• Cross-domain intelligence - Infrastructure + business context

/agents/agi_agent/
├── reasoning_engine.py       # Core AGI reasoning
├── knowledge_graph.py        # Context and relationship mapping
├── meta_learner.py          # Adaptive learning system
├── transfer_learning.py     # Cross-domain knowledge transfer
└── decision_optimizer.py    # Multi-objective optimization

2. MLOps Pipeline (/mlops/)

Machine Learning Operations

• Model training - Train prediction models for blast radius, cost optimization
• Model versioning - Track and manage ML model versions
• A/B testing - Test models in production safely
• Model monitoring - Performance tracking and drift detection
• Feature store - Centralized feature management
• Auto-retraining - Continuous model improvement

/mlops/
├── model_registry.py         # Model versioning and storage
├── training_pipeline.py      # Automated training workflows
├── feature_store.py          # Feature engineering and storage
├── model_monitor.py          # Performance monitoring
├── ab_testing.py             # Model A/B testing
├── drift_detector.py         # Data/model drift detection
└── auto_retrain.py           # Automated retraining triggers

ML Models to Implement:

• Blast radius prediction neural network
• Cost optimization recommender
• Anomaly detection for infrastructure
• Policy suggestion engine
• Intent classification model

3. DataOps Framework (/dataops/)

Data Operations & Pipeline Management

• Data ingestion - Multi-source data collection
• Data quality - Validation and cleansing
• Data lineage - Track data flow and transformations
• Data versioning - Version control for datasets
• Real-time streaming - Event stream processing
• Data catalog - Searchable data inventory

/dataops/
├── data_ingestion/
│   ├── connectors.py         # Multi-source connectors
│   ├── streaming.py          # Real-time data streams
│   └── batch_processor.py    # Batch data processing
├── data_quality/
│   ├── validators.py         # Data validation rules
│   ├── cleansing.py          # Data cleaning
│   └── profiler.py           # Data profiling
├── data_lineage/
│   ├── tracker.py            # Lineage tracking
│   └── visualizer.py         # Lineage visualization
└── data_catalog/
    ├── metadata_store.py     # Data catalog
    └── search.py             # Data discovery

4. DevOps Integration (/devops/)

CI/CD and Infrastructure as Code

• GitOps workflows - Git-based deployment automation
• Infrastructure as Code - Terraform, Ansible integration
• CI/CD pipelines - Automated testing and deployment
• Container orchestration - Docker, Kubernetes
• Observability - Logging, metrics, tracing
• Secret management - Secure credential handling

/devops/
├── cicd/
│   ├── pipeline_generator.py # Dynamic pipeline creation
│   ├── test_runner.py        # Automated testing
│   └── deployment.py         # Deployment automation
├── iac/
│   ├── terraform_adapter.py  # Terraform integration
│   ├── ansible_adapter.py    # Ansible playbooks
│   └── cloudformation.py     # AWS CloudFormation
├── observability/
│   ├── logging.py            # Centralized logging
│   ├── metrics.py            # Metrics collection
│   └── tracing.py            # Distributed tracing
└── secrets/
    ├── vault_integration.py  # HashiCorp Vault
    └── key_manager.py        # Key management

5. NLU (Natural Language Understanding) (/agents/nlu_agent/)

Advanced Intent Understanding

• Contextual understanding - Multi-turn conversations
• Entity extraction - Extract infrastructure entities
• Sentiment analysis - Understand urgency and emotion
• Intent classification - Map requests to actions
• Dialogue management - Conversational state tracking

/agents/nlu_agent/
├── intent_classifier.py      # Deep intent classification
├── entity_extractor.py       # Named entity recognition
├── context_manager.py        # Conversation context
├── sentiment_analyzer.py     # Emotional tone detection
└── dialogue_manager.py       # Multi-turn dialogue

6. NLP (Natural Language Processing) (/agents/nlp_agent/)

Language Processing Capabilities

• Text generation - Generate human-like respons...

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