The architecture gap your AI agent will expose

AI agents introduce failure modes that traditional MLOps practices aren't equipped to handle. Unlike ML models, agents maintain state, construct dynamic execution paths, and can take autonomous actions with unpredictable consequences. Key architectural considerations include: distinguishing AgentOps from MLOps (state management, tool orchestration, behavioral evaluation), using isolated 'testing pods' to observe agent behavior before production exposure, explicitly defining AI-writable versus human-controlled system boundaries (financial records, access controls, and infrastructure configs should remain off-limits), and building three-layer observability covering execution, reasoning, and performance logs. Engineering leaders are urged to treat agent infrastructure as a first-class architectural concern rather than retrofitting safety mechanisms after incidents occur.