@nicos_ai: A SENIOR ENGINEER AT ANTHROPIC JUST PUBLISHED A 12-PAGE PDF ON "GRAPH ENGINEERING" FOR MULTIAGENT SYSTEMS The mindset s…

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A senior Anthropic engineer published a 12-page PDF detailing a graph engineering approach for multiagent systems, using knowledge graphs as persistent shared memory to overcome context window limitations.

A SENIOR ENGINEER AT ANTHROPIC JUST PUBLISHED A 12-PAGE PDF ON "GRAPH ENGINEERING" FOR MULTIAGENT SYSTEMS The mindset shift: your agents' memory dies with their context window. A knowledge graph makes it permanent. Extract → Resolve → Assemble → Query → Repeat The 5 phases of every agentic graph: → Extract: Haiku pulls out entities and subject-predicate-object triples. One call per document. The Pydantic schema is the only training data. → Resolve: Sonnet groups "Edwin Aldrin" with "Buzz Aldrin" without any text overlap, using descriptions as context. → Assemble: canonical nodes, typed edges, and provenance in every triple. A single connected graph. → Query: serialize a subgraph, Sonnet reasons over the triples, and every response cites a specific edge. → Repeat: every new document re-enters the same pipeline and merges with existing nodes. The graph doesn't get rebuilt, it grows. Connect it to your multiagent systems as shared memory. Workers write to it, evaluators check against it, and loops stay alive from one day to the next. This 12-page PDF has changed how I'm building multiagent systems. Read it and then check out the article below
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A SENIOR ENGINEER AT ANTHROPIC JUST PUBLISHED A 12-PAGE PDF ON “GRAPH ENGINEERING” FOR MULTIAGENT SYSTEMS

The mindset shift: your agents’ memory dies with their context window. A knowledge graph makes it permanent.

Extract → Resolve → Assemble → Query → Repeat

The 5 phases of every agentic graph:

→ Extract: Haiku pulls out entities and subject-predicate-object triples. One call per document. The Pydantic schema is the only training data.

→ Resolve: Sonnet groups “Edwin Aldrin” with “Buzz Aldrin” without any text overlap, using descriptions as context.

→ Assemble: canonical nodes, typed edges, and provenance in every triple. A single connected graph.

→ Query: serialize a subgraph, Sonnet reasons over the triples, and every response cites a specific edge.

→ Repeat: every new document re-enters the same pipeline and merges with existing nodes. The graph doesn’t get rebuilt, it grows.

Connect it to your multiagent systems as shared memory.

Workers write to it, evaluators check against it, and loops stay alive from one day to the next.

This 12-page PDF has changed how I’m building multiagent systems.

Read it and then check out the article below

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A curated collection of research papers, benchmarks, and open-source projects on Graph Engineering in the era of LLM Agents, accompanying an arXiv survey paper to advance research from individual to system intelligence.