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All digests tagged Knowledge Graphs
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DDC is a methodology that uses AI agents to fail on real production incidents, systematically identifying and curating the minimum necessary knowledge context. This approach treats incident data as knowledge tests, moving beyond static runbooks to a 'living context.'
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In order to get reliable, uninterrupted access to the public web data on the internet, the knowledge layer needs to get the information from specialized tools. This is where the web data infrastructure layer comes into play.
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The web is no longer just a source of data; we can look at it as a source of context for knowledge work using AI agents. This leads to Context-as-a-Service (CaaS).
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AI success hinges on Forward Deployed Engineers mapping complex human workflows onto existing enterprise systems. The solution involves building agents on top of legacy platforms, using dependency graphs, and post-training models for context extraction.
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After initial multi-agent pipelines failed due to context loss, ZS Associates rebuilt their pharma analytics system by using a Knowledge Graph as a control plane and consolidating reasoning into a single agent.
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LLM synthesis destroys the paper trail. Learn how to build provenance into knowledge graphs using Graphiti, ensuring every derived fact can be traced back to its original source for compliance and debugging.
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LLMs are probabilistic; they need external, formal ontologies to act as logical guardrails. Neurosymbolic AI combines LLM power with structured logic (RDFS/OWL) for reliable agent behavior.
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