## DACA Real-World Examples

DACA’s flexibility makes it applicable to a wide range of agentic AI systems, from content moderation to healthcare, e-commerce, and IoT. Below are four examples showcasing how DACA can be implemented across different domains, following its progressive deployment stages (local → prototyping → medium scale → planet-scale) and leveraging its core components (EDA, three-tier architecture, stateless computing, CronJobs, HITL).

### Example 1: Content Moderation Agent
**Scenario**: A social media platform needs an agentic AI system to moderate user-generated content (e.g., posts, comments) for inappropriate material.

- **Local Development**:
  - Build the moderation agent using the OpenAI Agents SDK in a Docker container, with a Dapr sidecar for state management (local Redis) and messaging (local RabbitMQ).
  - Test the agent locally with Rancher Desktop, simulating posts and flagging content based on predefined rules (e.g., profanity detection).
  - Use FastAPI to expose a REST endpoint for submitting posts and retrieving moderation results.
- **Prototyping**:
  - Deploy to Hugging Face Docker Spaces (free tier) for public testing.
  - Use CloudAMQP (RabbitMQ free tier) for event-driven flagging (e.g., "PostFlagged" events) and Upstash Redis for caching moderation rules.
  - Schedule nightly rule updates with cron-job.org to fetch new keywords from a mock external source.
  - Use Dapr Managed Services from Diagrid (Free tier) 
- **Medium Scale (ACA)**:
  - Move to Azure Container Apps (ACA) to handle thousands of posts/hour.
  - Store moderation logs in CockroachDB Serverless, scaling to paid tier if needed.
  - Use HITL: Low-confidence flags (e.g., <90% confidence) trigger "HumanReviewRequired" events, routed to a Chainlit dashboard for moderators to approve/reject.
- **Planet-Scale (Kubernetes)**:
  - Deploy on Kubernetes with a self-hosted LLM (e.g., Mistral via vLLM or Meta Llama 3) to process millions of posts/day, avoiding OpenAI API limits.
  - Use Kafka for high-throughput messaging and Kubernetes CronJobs for nightly model retraining with human feedback.
  - HITL feedback improves the agent over time, reducing false positives.
- **DACA Benefits**:
  - EDA ensures real-time flagging, stateless containers scale with traffic, and HITL maintains accuracy.

### Example 2: Healthcare Diagnosis Assistant
**Scenario**: A telemedicine platform uses an agentic AI system to assist doctors by providing preliminary diagnoses based on patient symptoms, with human oversight for final decisions.

- **Local Development**:
  - Develop the diagnosis agent with OpenAI Agents SDK, running in a Docker container with a Dapr sidecar.
  - Use a local Postgres container to store patient data (symptoms, history) and Redis for caching medical knowledge graphs.
  - Test locally with Rancher Desktop, simulating patient inputs (e.g., "fever, cough") and generating diagnosis suggestions (e.g., "Possible flu, 80% confidence").
- **Prototyping**:
  - Deploy to Hugging Face Docker Spaces for free testing with a small user base (e.g., 100 patients/day).
  - Use CockroachDB Serverless (free tier) for patient data persistence and Upstash Redis for caching.
  - Emit "DiagnosisGenerated" events via CloudAMQP RabbitMQ, triggering notifications to doctors for review.
  - Managed Dapr Service Diagrid Catalyst (Free-Tier)
- **Medium Scale (ACA)**:
  - Scale to ACA to handle thousands of patients/day, auto-scaling on HTTP traffic.
  - Use HITL: Diagnoses with <90% confidence trigger "HumanReviewRequired" events, sent to a Streamlit dashboard where doctors confirm or adjust the diagnosis.
  - Store doctor feedback in CockroachDB to track accuracy and improve the agent.
- **Planet-Scale (Kubernetes)**:
  - Deploy on Kubernetes with a self-hosted LLM (e.g., Google Gemma, or OpenAI upcoming Open Source Model) to process millions of diagnoses/day.
  - Use Kafka for event streaming (e.g., "DiagnosisGenerated," "DoctorFeedbackReceived") and Neo4j (Graph DB) to store medical knowledge graphs for faster inference.
  - Kubernetes CronJobs run weekly to retrain the LLM with doctor feedback, improving diagnostic accuracy.
- **DACA Benefits**:
  - HITL ensures patient safety, EDA enables real-time doctor notifications, and stateless containers scale with patient volume.

### Example 3: E-Commerce Recommendation Engine
**Scenario**: An e-commerce platform employs an agentic AI system to recommend products to users based on browsing history, with scheduled updates to recommendation models.

- **Local Development**:
  - Build the recommendation agent using OpenAI Agents SDK in a Docker container, with Dapr for state (Redis) and messaging (RabbitMQ).
  - Use a local Postgres container to store user browsing history and product catalogs.
  - Test with Rancher Desktop, simulating user actions (e.g., "viewed electronics") and generating recommendations (e.g., "suggest laptops").
- **Prototyping**:
  - Deploy to Hugging Face Docker Spaces for free testing with early users.
  - Use CockroachDB Serverless to store user data and Upstash Redis to cache recommendations for low-latency access.
  - Emit "UserAction" events (e.g., "ProductViewed") via CloudAMQP RabbitMQ, triggering the agent to update recommendations in real-time.
  - Managed Dapr Service Diagrid Catalyst (Free-Tier)
- **Medium Scale (ACA)**:
  - Scale to ACA to handle thousands of users/hour, auto-scaling on user traffic.
  - Use Pinecone (Vector DB) to store product embeddings for faster similarity searches in recommendations.
  - Schedule daily model updates with ACA Jobs, pulling user behavior data to refine recommendations.
- **Planet-Scale (Kubernetes)**:
  - Deploy on Kubernetes with a self-hosted LLM to process millions of users/day.
  - Use Kafka for high-throughput event streaming (e.g., "UserAction," "RecommendationGenerated") and Redis on Kubernetes for caching.
  - Kubernetes CronJobs run nightly to retrain the recommendation model with user feedback, ensuring relevance.
- **DACA Benefits**:
  - EDA drives real-time recommendations, CronJobs keep models fresh, and stateless containers handle peak shopping traffic (e.g., Black Friday).

### Example 4: IoT Smart Home Automation
**Scenario**: A smart home system uses agentic AI to automate devices (e.g., lights, thermostat) based on sensor data, with human overrides for critical actions.

- **Local Development**:
  - Develop the automation agent with OpenAI Agents SDK in a Docker container, using Dapr for state (Redis) and messaging (RabbitMQ).
  - Simulate sensor data (e.g., "temperature 28°C") in a local Postgres container, testing actions like "turn on AC."
  - Use FastAPI to expose endpoints for manual overrides (e.g., "turn off lights").
- **Prototyping**:
  - Deploy to Hugging Face Docker Spaces for free testing with a small number of homes.
  - Use CockroachDB Serverless to store device states and Upstash Redis to cache sensor readings.
  - Emit "SensorTriggered" events (e.g., "MotionDetected") via CloudAMQP RabbitMQ, prompting the agent to act (e.g., "turn on lights").
  - Managed Dapr Service Diagrid Catalyst (Free-Tier)
- **Medium Scale (ACA)**:
  - Scale to ACA to manage thousands of homes, auto-scaling on sensor event volume.
  - Use HITL: High-impact actions (e.g., "unlock door") trigger "HumanApprovalRequired" events, sent to a Next.js dashboard for homeowner approval.
  - Store event logs in CockroachDB for auditing.
- **Planet-Scale (Kubernetes)**:
  - Deploy on Kubernetes with a self-hosted LLM to handle millions of homes/day.
  - Use Kafka for event streaming (e.g., "SensorTriggered," "ActionTaken") and Neo4j to store device relationships (e.g., "thermostat controls bedroom").
  - Kubernetes CronJobs run hourly to analyze usage patterns and optimize automation rules (e.g., "turn off lights if no motion for 30 minutes").
- **DACA Benefits**:
  - EDA ensures real-time device control, HITL adds safety for critical actions, and stateless containers scale with IoT device growth.

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