Why Your Enterprise Tech Stack Isn’t Ready for AI Agents — Christopher Lovejoy & Saul Howard
The talk addresses why traditional enterprise tech stacks are insufficient for deploying AI agents in highly regulated industries like healthcare. The core argument is that focusing on achieving high accuracy during a Proof of Concept (POC) often leads to architectural debt when attempting productionization. To build scalable, compliant systems, engineers must prioritize non-functional requirements—specifically auditability, data security, and human oversight—from the outset. This requires adopting specialized primitives: immutable event logs, schema-driven object storage for sensitive data, and treating humans and models as equivalent agents.
Key takeaways
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Audit Trail vs. Developer Log
0:05
In regulated environments (e.g., HIPAA, SOC 2), an audit trail must be a complete record of every action taken by the agent, every place it accessed data, and the authorization behind each step—not merely a developer log like those found in DataDog [5:19].
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Prioritize Constraints Over Accuracy
0:12
Engineers should take regulatory constraints seriously first (e.g., auditability) and design the architecture around them, rather than bolting compliance requirements onto a high-performing POC [12:07].
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The Three Architectural Primitives
0:08
Effective AI agent systems require three core primitives: an immutable append-only event log (for state tracking), schema-driven object storage (for data separation and Zero Trust), and human/model agent equivalency (for seamless escalation) [8:30].
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Evals as a Byproduct
0:10
By implementing these three primitives, robust evaluation (evals) can emerge naturally—allowing for action replay, testing on production data without exposure, and comparing human vs. model performance—rather than being an afterthought [10:37].
