Agentic workflows that function perfectly locally often fail in production due to critical engineering gaps. These failures stem from memory leakage, evaluation blindness, and tooling fragility, not inherent complexity. LLMs are stateless, making context window limitations and session memory a significant hurdle in production. Naive prompt accumulation overwhelms context windows, leading to increased latency, cost, and degraded reasoning quality.Production agents require a hybrid memory system with short-term buffers, medium-term vector embeddings, and long-term structured data. Traditional unit tests are insufficient for LLMs; production requires LLM-as-a-judge evaluation and golden datasets for regression testing. Tooling fragility arises from unhandled API errors, network issues, and schema changes, necessitating robust retry mechanisms and circuit breakers.Tool orchestration must include exponential backoff for retries, circuit breakers for external dependencies, and schema validation to prevent errors. Effective observability through trace-level logging, cost tracking, and human-in-the-loop escape hatches is crucial. Bridging these gaps shifts focus from prompt engineering to agent systems engineering, demanding discipline in memory management, testing, tooling, and observability. Production readiness involves implementing these patterns and establishing fallback mechanisms and security audits for every tool call.