Plexara
Learning / Insights
Technical Perspectives
Field notes
What these are, and what they are not
These are editorial pieces. Each one captures a single architectural argument, a comparison against an alternative approach, or a perspective on where the market is going. They are written to be read in any order; each piece stands alone.
Use insights when you want context for a decision, not when you need a procedure. Reference material lives in the 100 and 200 series of the curriculum.
Why proximity matters: tools, meaning, and memory belong together
Most AI agent stacks are gateways wrapped in auth. The hard work is not routing tool calls; it is making sure context arrives with them.
The context gap in AI data access
AI agents can execute SQL, but without business context they generate inaccurate queries and untrustworthy results. Not a better model. Better context.
Protocols outlast products
MCP, Trino, and DataHub are open protocols with communities larger than any vendor. Building on protocols, not proprietary platforms, is the durable choice.
How knowledge application turns usage into documentation
Most data catalogs are empty because documentation is a separate task. Plexara inverts this: documentation happens as a byproduct of people using data.
Governance at execution time vs. catalog time
Traditional governance creates policies in a catalog and hopes they are enforced. AI agents expose the gap. Closing it unifies governance with execution.
Token efficiency in enterprise MCP deployments
Most MCP implementations waste tokens through tool explosion, redundant metadata fetches, and repeated context. Three mechanisms eliminate these costs.
Replacing the five-vendor data stack with one platform
The modern data stack costs $300K-$1M per year across 5+ products. Plexara consolidates catalog, query, governance, enrichment, and agent framework into one.
Why MCP gateways are not enough
MCP gateways solve the plumbing problem but not the meaning problem. A gateway authenticates a tool call. It cannot tell you what the data means.
Why incumbent AI assistants are not enough
Every major warehouse vendor has an AI assistant. They work well within their own ecosystem. The problem is that your data does not live in one ecosystem.
Why point-solution catalogs and semantic layers are not enough
Data catalogs document data but cannot execute queries. Semantic layers define metrics but delegate execution. Neither provides unified context and access.