Engineering a reliable AI system isn’t as easy as calling an API. As AI applications move from prototypes to production, you must solve system-level problems the model alone cannot solve: retrieval quality, tool execution, hallucinations, validation, latency, cost, failures, observability, and operational control.




Systems Thinking for Agentic AI gives engineers and architects a practical framework for tackling these challenges. You’ll first understand how LLMs work through tokens, embeddings, and transformers, then learn to control their behavior with prompting, structured outputs, and decoding strategies. You’ll connect models to real systems using function calling, tools, and MCP, and build grounded RAG pipelines with embeddings and vector databases.




As you progress, you’ll design agentic workflows with planning, memory, orchestration, and controlled execution. You’ll implement guardrails, permissions, validation, human approval, evaluation, regression testing, and observability through logs, metrics, and traces. You’ll also address production concerns including scaling, performance, cost, timeouts, retries, and fallbacks.




Finally, you’ll bring these principles together by building an end-to-end Code Review Agent with Spring Boot, giving you a practical blueprint for engineering AI systems you can confidently operate in production.