Linear AI agent pipelines are increasingly being deployed in production environments, yet many face significant challenges that lead to their collapse. One of the primary issues is KV cache bloat, which can severely hinder performance and efficiency.
Another contributing factor is prompt cache churn, which disrupts the stability of these pipelines. When prompts are constantly changing, it can lead to inconsistencies and failures in the agent's responses.
Additionally, the presence of unverified AI code poses a major risk. Without thorough verification, the reliability of AI agents in production cannot be guaranteed, leading to potential failures and operational setbacks.