Why Your Enterprise Tech Stack Isn't Ready for AI Agents - And What to Build Instead

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Agent-executed work is a new infrastructure primitive. Until you treat it that way, you're running a

demo, not enterprise AI. Your existing stack was built for deterministic software. Agents reason,

delegate, and make judgment calls. That distinction creates infrastructure problems most engineering

teams haven't confronted: security vulnerabilities baked in by design, no audit trail, no

explainability, no human-in-the-loop. At Anterior, we've deployed clinical AI agents across many of

the largest US health plans, covering 50 million lives. Healthcare, with high stakes, strict

regulation, deeply human workflows, exposes infrastructure gaps that exist everywhere - and makes

the paradigm shift unavoidable: agent-executed work as a first-class primitive, alongside compute,

storage, and APIs. We'll cover why bolting agents onto existing data pipelines fails, what

infrastructure primitives are missing (and why teams don't notice until an audit), and how to

architect a stack where security, compliance, and human oversight are load-bearing from day one. If

you're serious about agents in any mission-critical context, this is the infrastructure conversation

you need to have.

Related YouTube Video

Make your LLM app a Domain Expert: How to Build an Expert System — Christopher Lovejoy, Anterior (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

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Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

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