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AI agent memory systems store preferences as flat files with no relationships. When context changes, nothing flags stale memories for review. We noticed this while building our own site — and realized it's the same problem Lattice solves for code.
Your agent passes every lint check, writes clean tests, and ships code that solves a problem nobody asked it to solve. The failure isn't in execution. It's in intent.
Context engineering is the term of the moment. But most approaches focus on runtime — what fills the context window for each LLM call. The missing piece is upstream: where does the knowledge come from, and is it still valid?
Lattice gives you traceability. But traceability assumes you know what you're looking for. QMD adds the missing piece: semantic search over your knowledge graph, running entirely on-device.