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Everystrong AI application starts with data, but the most useful systemsincreasingly depend on something more: the relationships, history, and contextsurrounding that data.
That’s becoming especially important as developers build agentic AI systems. Ratherthan treating context as a single prompt or one-time retrieval step, these systems can continuously assemble and update context as different agentscomplete tasks, use tools, and pass information between one another. In effect,context becomes a form of shared memory.
This is where graph technology is finding an interesting role. Graphs are designed torepresent connections between people, places, events, tasks, decisions, and other pieces of information. Instead of retrieving isolated chunks of text, anAI system can use those connections to follow relationships across multiplesteps, retain history, and better understand how different pieces ofinformation relate.
That approach is behind concepts such as GraphRAG, which combines large languagemodels with graph-structured data. It can be particularly useful for questionsinvolving multiple relationships, personalization, or understanding how aprevious decision was reached.
Thebroader shift is toward what’s sometimes called context engineering: designinghow AI systems gather, organize, maintain, and use the information they need toreason effectively. Knowledge graphs are one way developers are experimentingwith giving AI systems a richer picture of the data—and the story connectingit.
Forthose interested in exploring the idea further, Neo4j offers a free, self-pacedContext Engineer learning path through GraphAcademy, covering graphfundamentals and their use in AI applications.
👉 https://graphacademy.neo4j.com/categories/context-engineer