Markdown source

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

Conference Context

Session Description

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.

Media Evidence

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).

Evidence Graph

This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

Media Signals

Agent Reading Notes

Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

Transcript Status

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.

People

Supporting Slides

Synthesis

Synthesized Breakdown

Why Your Enterprise Tech Stack Isn't Ready for AI Agents - And What to Build Instead ## Conference Context - Date/time: 2026-07-01 · 3:45pm-4:05pm - Track/room: AI in Healthcare · Track 7 - Speaker(s): Christopher Lovejoy, Saul Howard - Session type/status: session · confirmed - Track: AI in Healthcare - Room: Track 7 - Session type: session - Status: confirmed ## Session Description 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.

Speaker And Company Context

Topics Covered

Derived Links And Source Material

Novel Concepts / Clever Methods

Evidence Boundary

This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.