Edith Heroux

Deploying production-grade AI systems that maintain continuity across failures, updates, and scaling events requires methodical planning across multiple engineering domains. Organizations rushing into implementation without addressing foundational concerns around state management, fault tolerance, and operational observability often discover critical gaps only after costly production incidents. A comprehensive implementation checklist serves not as bureaucratic overhead, but as a distilled collection of hard-won lessons from teams who learned these requirements the expensive way. The complexity of Persistent AI Agents stems from their dual nature—they must function as both robust distributed systems and sophisticated reasoning engines. Traditional software deployment checklists address infrastructure, security, and monitoring, but miss the unique challenges of systems that accumulate knowledge over time, maintain conversational context across sessions, and make autonomous decisions ba...