Graph Engineering - The Loop Memory Model
English | 2026 | ISBN: 1808812476 | 138 pages | True PDF | 2.96 MB
Give agentic systems a memory that survives every run by turning decisions, evidence, and execution history into durable, traceable graph structures
Key Features Separate work history from durable knowledge with a two-graph architecture and clear bridge rules Retrieve small, provenance tracked context windows from graph paths within token budgets Run parallel workers with scoped promises, validated writes, reducer contracts, and hard budgets Book Description Agent loops are good at producing work, but the reasoning behind that work disappears the moment the context window closes. Two agents rediscover the same bug three sprints apart; a decision made in run 12 gets silently contradicted in run 47.
Graph Engineering moves that reasoning out of transcripts and into an explicit knowledge graph. You'll model execution history separately from durable knowledge, then connect the two with a small set of bridge relationships. Markdown stays the authoritative source; the graph is a disposable index built for queries, not a second source of truth.
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