In this module, you will step into the role of Lead Architect at Praxis AI to tackle complex orchestration challenges for two distinct clients. First, you will rescue the Urban Hop travel assistant by implementing the Planner-Executor pattern, separating high-level reasoning from deterministic execution to ensure reliability. Next, you will transition to a Site Reliability Engineer (SRE) role for Global Freight, using distributed tracing and observability to diagnose and fix race conditions in a high-concurrency logistics engine. By the end of the module, you will have mastered the architectural patterns necessary to build agentic systems that are not just intelligent, but predictable, scalable, and debuggable.

In this module, you tackle the complexity of persistent memory in distributed systems. You will act as a Systems Engineer for Global Freight to solve critical "Lost Update" race conditions using Redis and pessimistic locking. Simultaneously, you will serve as an AI Architect for Urban Hop, implementing Vector Stores (for use with Retrieval Augmented Generation, also known as RAG) and memory optimization strategies to give your agent long-term, semantic recall without blowing up the context window.

In this final module, we shift focus from functionality to viability. You will transition from an AI Architect to a Site Reliability Engineer (SRE) for Global Freight Co., addressing critical issues in a live staging environment. The system is currently technically operational but commercially unviable due to severe security vulnerabilities (PII leaks) and inefficient resource usage (high costs and latency). You will first secure the system using the Agents SDK's native guardrails to intercept and redact sensitive data. Then, you will optimize performance by implementing caching strategies and model orchestration to reduce latency and costs, ensuring the system is ready for production deployment.