To bridge the "Agent Execution Gap"—the discrepancy between intended automation and production-grade reliability—ecommerce operators must prioritize systems architecture over agent quantity. While enterprise agent deployments doubled in 2025, many pilots fail due to missequenced deployment. Effective implementation requires a four-layer diagnostic approach: defining an Intent Layer with strict decision boundaries, implementing a Memory Layer using RAG (via vector databases like Pinecone or pgvector) to prevent context errors in tasks exceeding three steps, utilizing an Orchestration Layer (leveraging LangGraph for stateful workflows or Anthropic’s Model Context Protocol for multi-agent context sharing), and placing the Approval Layer at the intent-validation stage rather than post-execution. Successful sequencing—deploying memory and orchestration before expanding workflows—correlates with a 3.2x higher production survival rate. High-ROI, production-ready use cases include inventory replenishment (reducing overstock costs by 18–23%), customer service (resolving 55–70% of tier-1 tickets), and supplier communication (reducing manual PO processing by 74%). Conversely, fully autonomous merchandising and multi-agent systems coordinating over four tools without orchestration scaffolding remain experimental and risky.