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Context, Semantics, and Ontology: A Primer for the Agentic Era

Motherduck

A primer on the context layer, semantic layer, and ontology: what each term means, how they fit together, and why AI agents still need a human to decide what's true.

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Blogs Motherduck

BlogsWhy Use DuckDB for Analytics?Motherduck BlogsBuild sub-second data applications with MotherDuck’s Wasm SDKMotherduck BlogsThe Enterprise Case for DuckDB: 5 Key Categories and Why Use ItMotherduck BlogsHow to analyze SQLite databases in DuckDBMotherduck BlogsDuckDB 1.3 Lands in MotherDuck: Performance Boosts, Even Faster Parquet, and Smarter SQLMotherduck BlogsAI That Quacks: Introducing DuckDB-NSQL-7B, A LLM for DuckDB SQLMotherduck BlogsWhy do all LLMs need structured output modes?Fireworks BlogsIntroducing Supervised Fine-tuning V2Fireworks EventsCNCF Reveals KubeCon + CloudNativeCon North America 2026 Schedule, Adds New AI Inference + AgenticCncf BlogsAgentic ProgrammingMartinfowler BlogsWhat is MCP and how to get startedMintlify BlogsDesigning For Agentic AI: Practical UX Patterns For Control, Consent, And AccountabilitySmashingmagazine ResearchNo Need for Explanations: LLMs can implicitly learn from mistakes in-contextCohere BlogsVariable Sequence Length Training for Long-Context Large Language Models - CerebrasCerebras BlogsCerebras Brings Trillion Parameter Inference to Enterprises with Kimi K2.6Cerebras BlogsFrontier AI at a fraction of the cost: open-source worker agents with a closed-source advisor.Fireworks BlogsAnnouncing Kubescape 4.0 Enterprise Stability Meets the AI EraCncf BlogsOptimizing the OpenTelemetry Python SDK for LLM WorkloadsHoneycomb Products & ServicesWorking with Claude in GitBookGitbook BlogsThe Psychology Of Trust In AI: A Guide To Measuring And Designing For User ConfidenceSmashingmagazine