Safety & Permissions

Scalability & High Traffic: Stateless agent microservices are deployed on Kubernetes, utilizing KEDA to auto-scale horizontally based on incoming API request queues and CPU utilization.
Availability & Resilience: The architecture spans Multi-AZ (Availability Zones) to ensure 99.99% uptime. For LLM resilience, we employ a multi-model fallback strategy (e.g., defaulting to GPT-4o, but instantly falling back to Claude 3.5 Sonnet if API timeouts occur).
Fault Tolerance & Reliability: Asynchronous task processing is managed via Kafka/RabbitMQ. If a tool sandbox crashes mid-execution due to out-of-memory errors on a massive dataset, the task is routed to a Dead Letter Queue (DLQ). The system automatically retries the task in a larger container tier without dropping the user's request.
Performance Metrics: Implemented semantic caching (Redis + Vector embeddings). If a new query is semantically 98% similar to one asked 2 minutes ago, the cached insight is returned instantly. Key tracked metrics include Time To First Token (TTFB), Task Completion Rate, Code Execution Time, and LLM Token Cost per Session.

Idempotent Case: Complex analysis can be computationally expensive. We hash the User_Query + Current_Date_Window. If the user accidentally double-clicks submit or refreshes the page, the API Gateway recognizes the duplicate hash and safely returns the existing state/cached result, ensuring no redundant, heavy DB scans are executed.
Rate Limit Case: * User-Side: The API Gateway uses a Token Bucket algorithm to throttle abusive user requests, returning HTTP 429 with a "Retry-After" header.
- LLM-Side: The Orchestrator utilizes exponential backoff and jitter to gracefully handle rate limits from OpenAI/Anthropic, keeping the user informed via UI loading states rather than failing the chat session.