Blog
Notes on context for AI agents
How to launch reliable AI agents for your business — and why the context underneath matters more than the model.
Your ontology shouldn’t stop at the warehouse
Lakehouse-native ontology tools model the tables you already landed. The meaning your agents need lives in the operational systems upstream — and in the procedures no entity diagram captures.
Read moreHow to wire a knowledge agent to your company brain via MCP
Knowledge agents only work when they share one governed brain. Here is how any team wires agents to a company brain through MCP — without rebuilding platform infrastructure.
Read moreWhy your company needs context intelligence
The Modern Data Report 2026 shows a data activation gap is blocking AI. Stop thinking in terms of tables — think in terms of governed data products.
Read moreWhat makes up a governed data product
Links to each component: lexicon, thesaurus, ontology, process graph, consumption, governance, lineage, quality, and security.
Read moreLexicon: what things are called and what they mean
How the data product lexicon is built — schema, definitions, and ownership so everyone (and every AI) agrees on terms.
Read moreThesaurus: how terms relate across systems
How the data product thesaurus maps canonical terms to system-specific aliases so context is consistent everywhere.
Read moreOntology: how concepts connect
How the data product ontology defines entities and relationships so you can join and reason across the full context.
Read moreProcess graph: how data is produced, linked, and traced
How the process graph is built — entities, links, and decision traces for complete operational context.
Read moreConsumption: APIs, contracts, and how data is used
How data product consumption is defined — endpoints, SLAs, and access so data is usable by apps and AI.
Read moreGovernance: policies, access, and compliance
How data product governance is defined — tags, role-based access, and approvals.
Read moreLineage: where data comes from and where it goes
How data product lineage is captured — upstream sources, downstream consumers, and impact analysis.
Read moreQuality: rules, metrics, and how quality is ensured
How data product quality is defined and monitored — rules, freshness, and reliability so context is trustworthy.
Read moreSecurity: encryption, audit, and protection
How data product security is implemented — encryption, access control, and audit so context is protected.
Read moreWhy Claude Cowork Is Creating a New Kind of Data Chaos — and Why Governed Context Is Needed Yesterday
When everyone has their own "source of truth" for the AI, the model hallucinates in a different way for each person. The fix isn’t better prompts — it’s governed context.
Read moreOne context layer for AI agents: why loading Cursor or Cowork with org data isn’t enough
Teams get live data via MCP — but every system stays separate, data isn’t related, and each person builds their own context on their own machine. One shared context layer fixes that.
Read moreLoxtep is an Enterprise Context Layer — here’s what that actually means
The Enterprise Context Layer framework maps organizational knowledge into three substrate parts and five capabilities. Here is how Loxtep implements each, and why the category matters for building token capital.
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