Context Engineering: The Bottleneck Blocking Better Continuous AI

Despite rapid advances in AI technology, the problem of providing the right Context at the right time remains an unsolved challenge. In particular, as large organizations attempt to automate software development workflows through Continuous AI, Context Engineering is emerging as a major bottleneck.

Context Engineering goes beyond simply writing good prompts—it refers to the technique of systematically delivering the relevant information AI needs to perform a task. The context crisis that many organizations currently face arises for the following reasons.

• Undocumented information: Important decisions or the reasoning behind architectural designs exist only in meetings or conversations, never becoming data.
• Fragmented data: Information is scattered across various systems such as Confluence, Jira, and Slack, making it hard to access.
• Documentation not optimized for AI: Existing documentation is tailored to how humans read, making it difficult for AI to immediately understand and search.

In large-scale environments, selecting relevant context is also extremely difficult. There are physical constraints on the Context Window, and if incorrect information is included, it can cause Context Poisoning, a phenomenon where AI propagates errors. In addition, the maintenance problem of information quickly becoming outdated is also a key challenge that must be addressed.