8 engineering lessons on running AI agents in production

Deploying AI agents in production depends less on model selection than on established system design, operational knowledge, clean data, governance, and clear human workflows, according to lessons from Red Canary’s security platform. Its leaders recommend working backward from desired outcomes to identify decision points and required data, building modular single-purpose components so new agent capabilities can be tested on limited data without disrupting core systems, and automating deterministic tasks with conventional software before applying agents to well-understood but less scriptable work. Agents should have narrowly defined responsibilities, tools, expected outputs, oversight, KPIs, and access controls comparable to those assigned to new employees, while their training and tuning should rely on trusted examples shaped by expert human judgment. Organizations without extensive historical decision data should begin by documenting a small, familiar process and mapping its data flows, system boundaries, and human decisions in diagrams designed for team understanding, treating agent deployment as an extension of general systems engineering rather than a standalone AI initiative.

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