Context That Dreams: Context Engineering 2.0 [Testμ 2026]

At Testμ Conf 2026, Piramal Finance AI leader Jaydeep Chakrabarty presented “Context Engineering 2.0,” an approach intended to extend retrieval-based AI context layers by continuously deriving traceable conclusions from relationships among organizational records. He argued that conventional retrieval and GraphRAG systems can retrieve only explicitly recorded information, whereas a knowledge graph can connect dispersed signals—such as a skipped login test, increased logout complaints, and a session-timeout deployment—to identify a likely underlying defect. His Nodeex library builds such graphs through document acquisition, extraction, entity resolution, schema creation, and required human review, while preserving provenance for each source and derived claim. The system uses multi-hop relationships and eight analytical lenses, including contradiction, dependency, timing, and recurrence, to generate and score potential insights that downstream systems can use. Chakrabarty emphasized that generated ideas should remain grounded in graph evidence, with human feedback, access controls, masking, deduplication, and authorization serving as safeguards against hallucinations, poor-quality data, and inappropriate disclosure.

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