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This article addresses critical production challenges for AI agents, including authorization models, server-side guardrails, and the alignment of evaluation metrics with live performance. It offers practical insights from senior engineers on managing token costs and code velocity in systems where AI acts autonomously at scale. These topics are central to the operationalization of machine learning rather than incidental software updates.

Sources coveringInfoQ, AI Alignment Forum, Latent Space, AWS

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Production Patterns for Agents, Evals, and LLM Costs: QCon AI New York 2026

InfoQOctober 13, 20263 min read

Once AI is in production, code arrives faster than it can be reviewed, token costs rise, evals have to be good enough to act on, and agents need permissions and guardrails. QCon AI New York 2026 (December 15-16) has confirmed sessions from engineers sharing how they handle these constraints. By Artenisa Chatziou

Once AI is in production, code arrives faster than it can be reviewed, token costs rise, evals have to be good enough to act on, and agents need permissions and guardrails. QCon AI New York 2026 has confirmed sessions from senior engineers who have worked through these problems in systems they run. The conference takes place December 15-16 at The Westin Jersey City…

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