Engineering teams lose significant time because critical project knowledge is scattered across tools and documents. Every new joiner or project restart requires rebuilding the same understanding.
This problem is intensifying with AI-assisted development. According to the 2024 Accelerate State of DevOps Report, 75% of developers use AI daily, but when a session ends, the AI also loses project context.
At the same time, 46% of developers do not fully trust AI-generated code, largely because it lacks project-specific knowledge. Faster tools only accelerate this breakdown.
This is now a systems challenge, not merely a tooling issue. Without persistent project memory, teams incur a hidden cost through slower onboarding, fragile long-running work, and repeated re-explanation.
Teams lose time because project knowledge is rebuilt instead of reused. When work spans multiple tasks or projects, only about 40% of effort remains productive, with the rest spent restoring context.
This intensifies with agentic workflows. Coding agents like Claude Code rely on limited context windows and session-based memory. When sessions end or tools change, the agent loses awareness of architecture, decisions, and constraints, and each task starts with near-zero context.
This is reflected in real usage in the Stack Overflow Developer Survey 2025, 66% of developers said their biggest frustration with AI tools is that results are “almost right, but not quite.” The main reason is the missing project context.
Without a system to preserve and reuse knowledge, teams keep re-explaining the same things. Faster tools only make this cycle repeat faster.
Claude Code can read markdown files that persist across sessions. ByteRover also stores knowledge as markdown. The distinction is not the file format, but how context is curated, discovered and applied.