Plexara
Learning
AI, MCP, and the Governed Context Layer
Insights
Technical perspectives
Architecture decisions, comparisons against the alternatives, and arguments about where this market is going. Each piece stands alone, so start with whichever question you brought.
The report that runs without the agent
An hour with an AI assistant produces the perfect sales report. Re-deriving it every week burns tokens on logic that is already settled. Plexara lets the agent save that logic as a script the platform runs on demand or on a schedule: reports, exports, and dashboards that stay fresh with no model in the loop.
The spreadsheet that joins your warehouse
Ad-hoc CSVs are the most common data silo in business: valuable exactly when joined, stranded in inboxes because loading them was a project. Plexara registers an uploaded CSV, or one an agent built, as a queryable table over the file where it sits, so the join runs in the warehouse instead of the context window.
When the answer is an action
Business intelligence has always ended at a finding: the analysis stops, and the action moves to other tools, other people, and next week. An agent that reaches remote systems through a governed API gateway closes that gap. The same session that finds the audience pushes it, schedules the send, and verifies the result.
The curriculum
From what a token is to procedures your team runs
Work through it and a new analyst can get real answers out of the agent, know why the platform gave them that answer and not another, and turn the sessions worth repeating into procedures anyone on the team can run. Start at 101 if AI is new to your people, jump to the 200s if you know models but not MCP, and go straight to the 300s if you are already working in the portal.
AI Concepts
Foundations
Plain-language groundwork: what a large language model actually does, what a token costs you, what a frontier model is, and what people mean by an agent.
Plexara MCP
The platform curriculum
What an MCP server contains, what Plexara adds on top of the open standard, and how semantic enrichment, memory, and governance change what an agent can answer.
Asset Workflows
The applied workbook
Day-to-day mechanics of the asset system: building reports and dashboards, exporting data, sharing, collections, editing, metadata, and retrieval.
Prompts as SOPs
Procedures worth keeping
A report you went back and forth on is a procedure. Let the agent author it, share it with the team, improve it through feedback, and run it whenever you need it.
Spreadsheets as Tables
Data in, without a pipeline
A file somebody receives by email becomes a table every agent on the team can join against the warehouse. Uploading, registering, teaching the agent what the columns mean, joining and sharing, and keeping it current month after month.
Automations
The agent as developer
The agent writes the script, Plexara runs it on demand or on a schedule, and the outputs refresh themselves. Authoring, outputs, running, what a run may do, and the weekly review that composes scripts, prompts, and knowledge.
Also here
Reference, cadence, and what changed
Platform Concepts
Ten expandable visual explainers of how the platform works, from semantic enrichment and memory to personas, audit, and federated SQL.
Browse the conceptsNewsletter
The Plexara Monthly Dispatch: what shipped, what is worth reading, and one practical tip. Every issue archived here in full.
Read the archiveChangelog
A weekly, plain-language record of new capabilities landing in your fully managed platform.
See what is newHow to use this curriculum
What each series leaves your team with
The 100 series is the foundation: tokens, context windows, frontier versus specialized models, what an AI agent actually is. Read these first if AI is new to your team or if you want to ground vocabulary before going deeper.
The 200 series is the platform curriculum. It covers what an MCP server actually contains, how Plexara extends the protocol with semantic enrichment, memory, and governance, and what a real engagement looks like end to end. Read these to understand how Plexara differs from a vanilla MCP gateway.
The 300 series is the applied workbook. Eight short lessons covering the day-to-day mechanics of the asset system: creating reports and dashboards, exporting data, sharing, collections, editing, metadata, retrieval, and writing reproducible prompts. Start here once you have the mental model from the 200 series.
The 400 series is the operating-procedure layer. It makes the case that a report you went back and forth on is a procedure worth keeping, then covers letting the agent author the prompt, sharing it across the team, improving it through feedback, and running it by hand or on a schedule. Read it once you are producing real work with the agent and want it to compound.
Insights sit outside the numbering on purpose. They are editorial, not reference: each one captures a single architectural argument, a comparison against an alternative approach, or a perspective on where the market is going. Read them in any order, whenever the question comes up.
Suggested paths
- AI-fluent reader: skim 110 (MCP vs APIs) and dive into the 200 series.
- Reader new to AI: 101, 102, 103, 105 build the foundation; then start the 200 series.
- Architect or platform lead: skim Insights first to understand the design philosophy, then go deep on the 200 series.
- Analyst or daily user: read 205 in the 200 series, then jump straight into Asset Workflows for the recipes.
- Standardizing recurring work: read Asset Workflows, then Prompts as SOPs to turn your best sessions into shared, repeatable procedures.
Next
Portal Tour
See how Plexara puts everything you have just learned into the hands of your teams.