Developer builds server-side memory preseeding to fix AI agent context amnesia

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A developer running nine parallel Claude Code sessions found that AI agents repeatedly failed not due to lack of intelligence but because they forgot previously documented project context. The initial fix — instructing agents via system prompts to call a memory search tool — worked inconsistently, with some sessions skipping the step entirely. To solve this, the developer moved memory retrieval into the server layer, so relevant project notes are automatically injected into each task before the model ever receives it. The system uses local embeddings, an incremental SQLite index, and markdown files with YAML frontmatter to store and retrieve context deterministically. An end-to-end test verifies the setup by storing facts nowhere in the codebase and confirming that only memory-backed sessions answer correctly.

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