Context Is the New Monolith: A Lesson from Reviewing an AI Agent Architecture

A team developed an AI agent platform for automating video generation, but the architecture had a major flaw: each agent received the entire execution context, not just what it needed. This created scalability issues and increased latency. In production, this can lead to problems like increased token consumption, larger responses, and higher inference time. To avoid this, agents should only receive relevant context. This is a common mistake in AI system design, especially in small systems that hide architectural flaws.