Action AI - stanlypoc/AIRA GitHub Wiki

Agentic AI Reference Architectures

1. AutoGPT (Autonomous Agents)

Use Case: Self-prompting AI for task automation
Reference Architecture:

• Data Layer:
  • Vector DB (Pinecone, Weaviate) + API Gateway (Kong, AWS API Gateway)
• Model Layer:
  • LLM Core (GPT-4, Claude 3) + Memory Module (LangChain)
• Orchestration:
  • Agent Scheduler (Airflow, Prefect) + Feedback Loop (Human-in-the-loop)
• Application Layer:
  • REST API (FastAPI) + UI Dashboard (Streamlit)

---

Diagram

flowchart TD
    subgraph Data_Layer["Data Layer"]
        A["Vector DB (Pinecone/Weaviate)"]-->B["API Gateway (Kong/AWS)"]
        C["Task Metadata (PostgreSQL)"]
    end

    subgraph Model_Layer["Model Layer"]
        D["LLM Core (GPT-4/Claude)"]-->E["Memory Module (LangChain)"]
        F["Feedback Analyzer"]
    end

    subgraph Orchestration["Orchestration"]
        G["Agent Scheduler (Airflow)"]-->H["Task Queue (RabbitMQ)"]
        I["Human-in-the-Loop"]
    end

    subgraph Application["Application"]
        J["REST API (FastAPI)"]-->K["UI Dashboard (Streamlit)"]
        L["Slack/Teams Bot"]
    end

    A-->D
    B-->J
    D-->G
    F-->I
    H-->J

2. AutoGen (Multi-Agent Collaboration)

Use Case: AI teams working collaboratively
Reference Architecture:

• Data Layer:
  • Vector DB (Chroma, Milvus) + Agent State DB (Redis)
• Model Layer:
  • Specialist Agents (CodeGen, Research, QA) + Orchestrator Agent (AutoGen)
• Orchestration:
  • Agent Communication Bus (RabbitMQ) + Conflict Resolution Engine
• Application Layer:
  • Multi-Agent Workspace (Jupyter Notebooks + Discord Bot)

---

3. GPT Engineer (Code-Generating Agents)

Use Case: AI that writes and deploys code
Reference Architecture:

• Data Layer:
  • Code Repo (GitHub, GitLab) + API Docs (Swagger)
• Model Layer:
  • Code LLM (GPT-4, CodeLlama) + Code Validator (SonarQube)
• Orchestration:
  • CI/CD Pipeline (GitHub Actions) + Auto-Deployer (Terraform)
• Application Layer:
  • DevOps Dashboard (Grafana) + CLI Tool

---

flowchart TD
    subgraph Data_Layer["Data Layer"]
        A["Code Repository (GitHub)"]-->B["API Docs (Swagger/OpenAPI)"]
        C["Dependency Graph (Neo4j)"]
    end

    subgraph Model_Layer["Model Layer"]
        D["Code LLM (GPT-4/CodeLlama)"]-->E["Code Validator (SonarQube)"]
        F["Security Scanner (Semgrep)"]
    end

    subgraph Orchestration["Orchestration"]
        G["CI/CD Pipeline (GitHub Actions)"]-->H["Auto-Deployer (Terraform)"]
        I["Error Feedback Loop"]
    end

    subgraph Application["Application Layer"]
        J["CLI Interface"]-->K["VSCode Plugin"]
        L["PR Review Dashboard"]
    end

    A-->D
    B-->E
    D-->G
    F-->I
    H-->L

Robotics & Embodied AI Reference Architectures

4. Tesla Optimus (Humanoid Robots)

Use Case: General-purpose robotics
Reference Architecture:

• Data Layer:
  • Sensor Fusion DB (TimescaleDB) + Edge Compute (NVIDIA Jetson)
• Model Layer:
  • Motion Planning (PyTorch RL) + Vision Model (ViT)
• Orchestration:
  • ROS 2 (Robot Operating System) + Fleet Manager
• Application Layer:
  • Real-Time Control Interface (Custom Tesla OS)

---

flowchart TD
    subgraph Data_Layer["Data Layer"]
        A["Sensor Fusion DB (TimescaleDB)"]-->B["Edge Compute (NVIDIA Jetson)"]
        C["3D Maps (USD Format)"]
    end

    subgraph Model_Layer["Model Layer"]
        D["Motion Planner (PyTorch RL)"]-->E["Vision Model (ViT)"]
        F["Grasping Controller"]
    end

    subgraph Orchestration["Orchestration"]
        G["ROS 2 (Robot OS)"]-->H["Fleet Manager"]
        I["Safety Monitor"]
    end

    subgraph Application["Application"]
        J["Real-Time Control UI (Tesla OS)"]-->K["Maintenance Alerts"]
        L["Over-the-Air Updates"]
    end

    A-->D
    B-->G
    E-->J
    F-->I
    H-->L

5. RT-2 (Vision-Language-Action Models)

Use Case: Robotics with natural language commands
Reference Architecture:

• Data Layer:
  • Vision DB (FAISS) + Robot Telemetry (InfluxDB)
• Model Layer:
  • VLM Core (PaLM-E) + Action Translator (PyTorch)
• Orchestration:
  • Task Planner (PDDL) + Safety Monitor
• Application Layer:
  • Voice Interface (Google Assistant SDK)

---

Autonomous Systems Reference Architectures

6. Tesla FSD (Self-Driving AI)

Use Case: Autonomous vehicles
Reference Architecture:

• Data Layer:
  • Sensor Data Lake (Iceberg) + HD Maps (GeoJSON)
• Model Layer:
  • Perception Stack (HydraNet) + Path Planner (PyTorch)
• Orchestration:
  • Real-Time Inference Engine (TensorRT) + OTA Updates
• Application Layer:
  • Driver UI (React) + Fleet Analytics (Tableau)

---

flowchart TD
    subgraph Data_Layer["Data Layer"]
        A["Sensor Data Lake (Iceberg)"]-->B["HD Maps (GeoJSON)"]
        C["Telemetry Stream (Kafka)"]
    end

    subgraph Model_Layer["Model Layer"]
        D["Perception Stack (HydraNet)"]-->E["Path Planner (PyTorch)"]
        F["Traffic Predictor"]
    end

    subgraph Orchestration["Orchestration"]
        G["Real-Time Inference (TensorRT)"]-->H["OTA Update System"]
        I["Fail-Safe Controller"]
    end

    subgraph Application["Application"]
        J["Driver UI (React)"]-->K["Fleet Analytics (Tableau)"]
        L["Emergency API"]
    end

    A-->D
    B-->E
    F-->G
    I-->L

7. Darktrace Antigena (AI Cybersecurity Agents)

Use Case: Autonomous threat response
Reference Architecture:

• Data Layer:
  • Threat Intelligence DB (Elasticsearch) + Network Logs (Kafka)
• Model Layer:
  • Anomaly Detector (LSTM) + Decision Engine (RL)
• Orchestration:
  • Response Automator (SOAR) + Explainability Module
• Application Layer:
  • SOC Dashboard (Splunk)

---

Key Cross-Cutting Components

1. Data Layer:
   • Feature Stores (Feast, Tecton) for real-time data
   • Unified Metadata Catalog (Amundsen)
2. Model Layer:
   • Model Registry (MLflow)
   • Explainability (SHAP, LIME)
3. Orchestration:
   • Workflow Engine (Kubeflow, Metaflow)
   • Agent Communication Protocol (gRPC, WebSockets)
4. Application Layer:
   • Low-Code UI (Gradio, Dash)
   • APIs (FastAPI, GraphQL)

---

Samsung "Neon 2.0" (Emotional AI Companion)

flowchart TD
    subgraph Data_Layer["Data Layer"]
        A["User Behavior DB (MongoDB)"]-->B["Emotion Corpus (Multimodal)"]
        C["Conversation History (Vector DB)"]
    end

    subgraph Model_Layer["Model Layer"]
        D["LLM Core (GPT-5)"]-->E["Emotion Classifier (PyTorch)"]
        F["Micro-Expression Analyzer"]
    end

    subgraph Orchestration["Orchestration"]
        G["Dialog Manager (Rasa)"]-->H["Ethics Guardrails"]
        I["Persona Consistency Engine"]
    end

    subgraph Application["Application Layer"]
        J["Holographic Display"]-->K["Mobile App Sync"]
        L["API for Smart Home"]
    end

    A-->E
    B-->F
    D-->G
    I-->J
    H-->L

Medtronic "AI Surgeon" (Future Robotics)

flowchart TD
    subgraph Data_Layer["Data Layer"]
        A["Patient Records (FHIR)"]-->B["Real-Time Sensor Stream (ROS 2)"]
        C["Surgical Atlas (3D Volumes)"]
    end

    subgraph Model_Layer["Model Layer"]
        D["Vision Model (MONAI)"]-->E["Haptic Feedback RL"]
        F["Procedural Knowledge Graph"]
    end

    subgraph Orchestration["Orchestration"]
        G["Robotic Control (dVRK)"]-->H["Emergency Stop System"]
        I["Surgeon Override Interface"]
    end

    subgraph Application["Application Layer"]
        J["AR Surgical HUD"]-->K["Operational Analytics"]
        L["FDA Compliance Logger"]
    end

    A-->D
    B-->G
    E-->I
    F-->J
    H-->L