When AI Becomes the Expensive Bottleneck✎ Edit

Agent deployments don't scale on marketing hype alone. In the field, we're seeing builds slow down or get shelved because token burn, GPU quotas, cloud egress and operational debt stack up lebih pantas than the value they return.

The real architecture mistake is pushing every task through an LLM. Predictable work should flow through parsers, rule engines, event-driven services and edge-classifiers, while LLM calls are gated behind clear need and fallback logik.