Why Running AI Agents in Production Is Harder Than Running a Web App
Treating an agent deployment like "a web app that happens to call an LLM" will backfire in production.
Traditional production infrastructure models, with stateless containers behind load balancers and autoscaling based on CPU or queue depth, are not sufficient for AI agents in production. These agents often operate on their own schedule and perform actions without being triggered by external requests. Additionally, AI agents possess persistent memory and can autonomously make changes to their environment, expanding their attack surface.
The operational challenges of running AI agents in production differ significantly from those of web applications, including capacity planning, versioning, and security.
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