QCon AI New York to Tackle Identity and Guardrails for Autonomous Agents

Conference chair says AI engineering has become systems engineering as program focuses on production challenges

By LineZotpaper
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QCon AI New York has announced 23 of more than 30 sessions for its December 2026 conference, with talks covering identity and authorization for autonomous agents, guardrails for operations agents, and evaluation of AI decision systems in production. The event, aimed at senior engineers and technical leaders, reflects a growing focus on making AI systems reliable, observable, and economical to operate.

The conference, scheduled for December 15–16 at The Westin Jersey City Newport, targets senior engineers, architects, and technical leaders building AI systems in production. Conference Chair Hien Luu noted that the program signals a broader shift: "AI engineering has become systems engineering." According to Luu, the challenge is moving from model behavior to system behavior, requiring agents with bounded execution authority, context management, and probabilistic models wrapped in deterministic control planes. Topics include harness engineering, continuous evaluation, observability, and policy enforcement as core infrastructure.

Keynote speaker Nancy Wang, CTO at 1Password, will explore identity and authorization for agents in production in a talk titled "When Software Becomes a User: Identity and Authorization for Agents in Production." Wang will examine how traditional identity models—designed for single human principals with stable roles and bounded sessions—break down when agents act for multiple users, invoke unforeseen tools, create subagents, and operate without supervision. The session will cover delegated authority across chains of agents, auditability for multi-hop tool calls, and techniques to grant agents sufficient access without exposing credentials.

Other confirmed sessions include a talk on guardrails for operations agents running on large-scale Kubernetes infrastructure by Ronak Nathani, as well as discussions on shared model-serving platforms and post-deployment evaluation of AI decision systems. The full program is available on the QCon AI New York website.

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Analysis

Why This Matters

  • The conference addresses critical challenges as AI agents move from prototypes to production, with identity and authorization for autonomous agents being pressing security concerns.
  • The shift to systems engineering suggests the industry is maturing, focusing on reliability, observability, and cost as first-class constraints.
  • Practical techniques for governing agent behavior could influence how organizations deploy autonomous systems safely.

Background

As companies deploy AI agents for tasks like code generation, customer service, and infrastructure management, ensuring these agents operate within safe boundaries has become a key engineering problem. Traditional identity and access control models were not designed for autonomous software actors that may act for multiple users, invoke arbitrary tools, and run without direct supervision. Conferences like QCon AI reflect a growing recognition that production AI requires infrastructure disciplines drawn from distributed systems, security, and site reliability engineering.

Key Perspectives

Conference organizers and speakers: Argue that new engineering disciplines—harness engineering, continuous evaluation, policy enforcement—are needed to make AI systems reliable and economical. They see agent authorization and guardrails as solvable infrastructure problems. Critics and skeptics: May point to the inherent unpredictability of probabilistic models and the difficulty of enforcing tight bounds on autonomous agents without crippling their utility. The gap between academic guardrails and real-world scale remains large.

What to Watch

  • Adoption of agent authorization frameworks after the conference, particularly for multi-agent chains.
  • Practical outcomes from the guardrails for operations agents session, especially for Kubernetes environments.
  • Industry response to the "systems engineering" framing for AI—whether it gains traction as a formal discipline.

Sources

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