Why Your Enterprise Tech Stack Isn’t Ready for AI Agents — Christopher Lovejoy & Saul Howard

Enterprise-ready AI agents require robust architectural foundations that prioritize compliance and security from the outset, rather than treating these as secondary additions to a proof-of-concept. Building in regulated sectors like healthcare necessitates an immutable ledger of actions to ensure complete auditability and a justifiable chain of evidence. Separating orchestration logic from sensitive data via schema-driven object storage enables Zero Trust principles, preventing unauthorized data access while maintaining observability. Furthermore, establishing human-agent equivalency allows for seamless escalation, where any agent-driven action can be performed by a human. Integrating these primitives transforms evaluation from an external, bolted-on process into a first-class system property, allowing for privacy-preserving performance monitoring on production data. Shifting from a proof-of-concept-first to an architecture-first approach ensures that AI systems remain scalable, secure, and compliant within complex enterprise environments.