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Graph Engineering -- The Loop Memory Model. Give agentic workflows durable memory, traceability, and shared knowledge with graphs Volodymyr Pavlyshyn

Książka w języku angielskim angielski
Graph Engineering -- The Loop Memory Model. Give agentic workflows durable memory, traceability, and shared knowledge with graphs Volodymyr Pavlyshyn - okladka książki

Graph Engineering -- The Loop Memory Model. Give agentic workflows durable memory, traceability, and shared knowledge with graphs Volodymyr Pavlyshyn - okladka książki

Graph Engineering -- The Loop Memory Model. Give agentic workflows durable memory, traceability, and shared knowledge with graphs Volodymyr Pavlyshyn - audiobook MP3

Graph Engineering -- The Loop Memory Model. Give agentic workflows durable memory, traceability, and shared knowledge with graphs Volodymyr Pavlyshyn - audiobook CD

Autor:
Volodymyr Pavlyshyn
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
138
Dostępne formaty:
     PDF
     ePub

Ebook
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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.
From there, the book turns the model into a working mechanism: parse structured repository data, extract only genuine prose, resolve entities, and assemble typed relations. Retrieve small, relevant neighborhoods instead of dumping an entire knowledge base into context. See how evaluations check supporting paths, how concurrent workers publish changes without corrupting shared state, and how a pipeline preserves lineage from objective to artifact.
The final section is judgment; which metrics reveal graph quality, how false merges and bad incentives produce confident errors, what this architecture costs, and when a simple ADR log or relational table is the better answer.

O autorze książki

Volodymyr Pavlyshyn writes about AI-driven software engineering, agent workflows, and durable engineering knowledge. Graph Engineering is the third book in his Clarity series, following The Clarity Engineer and The Clarity Forge. Across the series, he explores how engineering judgment, specifications, architecture records, and agent execution can be turned into explicit artifacts that survive tool and model changes. In this book, he extends that work from durable specifications to graph-based memory for multi-agent development.

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