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This article focuses on the operational challenges of integrating autonomous AI agents into production environments, specifically addressing risk management, guardrails, and the shift from passive code generation to active system interaction. It examines practical engineering strategies for verifying agent outputs and maintaining human oversight in high-stakes workflows involving customer accounts and distributed systems.

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Engineering Production Systems for an Agentic Era: QCon San Francisco 2026

InfoQOctober 2, 20265 min read

QCon San Francisco 2026 will bring together practitioners from Airbnb, OpenAI, Netflix, Honeycomb, and other engineering organizations to share how they are building, operating, and evolving production systems as AI agents take on a larger role. By Artenisa Chatziou

AI agents are changing more than the way code is written. They are becoming users of production systems, initiating customer actions, querying observability data, and influencing how software is tested and released. That creates practical questions for senior engineers. Which decisions can safely be delegated to an agent? What evidence is needed before agent-generated work reaches production? How should existing systems expose context without expanding risk? Where must experienced engineers retain direct control? QCon San Francisco 2026, taking place November 16–20, brings together practitioners from Airbnb, OpenAI, Netflix, Honeycomb, and other engineering organizations to share how they are answering these questions…

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