That progression is a clean way to frame it. Constrained generation, constrained tool calling, constrained memory. Same pattern applied one layer deeper each time. The temporal piece is underrated. Most teams get to typed entities and stop, but fact invalidation is what keeps
@akshay_pachaar
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Schema-First Ingestion for AI Retrieval Systems
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Schema-first at ingestion time is the right framing. Most teams discover this after building the retrieval layer and wondering why structured queries return noise. The 10/10/10 constraint in Graphiti helps too. Forces you to model the 80% that matters rather than attempting
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Typed Schemas for LLM Data Ingestion and Domain Context
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The hard part is that "what matters" is domain-specific and the LLM has no way to know that on its own. That is where typed schemas earn their keep. You encode what matters once, and every future ingestion inherits that judgment without the model having to rediscover it.
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Memory Management in AI Systems: MemGPT and Schema Design
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MemGPT is a great parallel. Paging between fast context and slow storage is essentially the same idea; memory needs an explicit policy, not just a bigger buffer. The schema approach pushes that decision even earlier, to ingestion time rather than retrieval time.
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Knowledge Graph Extraction and Schema Design for AI Systems
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Exactly. The extraction step decides everything downstream, and most systems leave it completely unguided. The moment you add typed entities and constrained edges, the graph stops behaving like a vector store and starts being queryable. Schema should be step one, not an
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Graphiti hybrid ontology: prescribed and learned schema discovery
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Graphiti supports both prescribed and learned ontology, so it can discover new types alongside your defined schema. The sweet spot is a hybrid, fixed schema for core domain, system proposes new types for patterns outside that boundary. Full auto-schema loops back to the
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Ontology-Constrained Memory as Reasoning Boundary for Agents
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Ontology-constrained memory is a great way to frame it. The key insight that clicked for me is that the schema is not just a filter on what gets stored, it is also a reasoning boundary. What the agent cannot represent, it cannot hallucinate connections about.
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LLM Multi-Hop Retrieval: Schema vs Sequential Queries
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That works when the graph is small and the hops are predictable. At scale, sequential LLM queries for every multi-hop retrieval add latency and token cost per question. The schema lets you answer it in one structured traversal instead of chaining open-ended lookups. Not
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Agent Architectures: Thin Orchestrators Over Direct Work
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This matches what's happening in agent systems beyond research too. You would find similar patterns in Claude Code as well. I think the best agent architectures keep converging on thin orchestrators that delegate instead of doing the work themselves.
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Agent Behavior: Each Line Must Change Agent Actions
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The fix is making every line answer "what does the agent do differently because of this?" If a line doesn't change behavior, it's decoration.