Plexara
What you will take away from this lesson
In 204 - Trino Query: analytics and insights, we covered how the agent executes queries and touched briefly on the fact that large results belong in an asset rather than the conversation. This lesson is the asset system itself: the third step of the discover-query-enrich workflow and the place where useful output goes to live.
Assets turn a chat transcript into shareable work product. A dashboard built during one session is still there the next day for a teammate who was not in the conversation. Being explicit about the asset path in your prompts is the single largest token-saver after good session hygiene.
Learning Objectives
- 01Explain what a Plexara asset is and why persisting outputs outside the conversation matters for both token budget and shareability.
- 02Name the kinds of assets a Plexara session can produce (interactive dashboards, reports, CSV exports, charts, markdown documents) and when to ask for each.
- 03Use save_artifact and manage_artifact (or invoke them implicitly through explicit prompt language) so the agent does not dump large outputs into the chat.
- 04Group related assets into a collection so a teammate can open a single link and see the entire briefing in the portal.
- 05Recognize that assets live in the Plexara portal and can be referenced, edited, and re-queried across sessions.
Where we are in the curriculum
If any term in this lesson feels unfamiliar, the 100 series is one click back. The 200 series assumes that mental model.
100 Series: the foundation
- 101What is a Large Language Model?Brilliant at language, blind about your data. Tokens, hallucination, the grounding problem.
- 102Tokens and your budgetSubscription-plan economics, session limits, and Plexara enrichment dedup.
- 103Context, compression, and memoryThe keep / compress / clear playbook and how memory carries across sessions.
- 104Frontier models, specialized models, and why enterprise AI uses bothThree knowledge sources (training, web search, tools). MCP as the exposure protocol.
- 105What is an AI agent?The think/call-tool/observe loop. Professor's knowledge, child's literalism.
- 110Is MCP just an API wrapper?MCP as an application layer. Spectrum from thin wrapper to full application server.
If a term in this lesson looks unfamiliar, back up to the 100 series. The 200 series assumes that mental model. Every row above is a direct link.
Why the asset system exists
A large dashboard or a detailed markdown report is expensive to put into a conversation, expensive to regenerate, and hard to share once it scrolls past in the chat. The asset system turns those outputs into persistent, viewable, editable objects stored in the Plexara portal. The agent writes the artifact once, saves it, and returns a link. You can view it, share it with a teammate, and come back later and ask the agent to edit it without starting over.
There is a second reason the asset system exists, and it is about discipline: keeping large outputs out of the conversation entirely. A dashboard that lives in the portal does not sit in the context window eating tokens on every subsequent turn. A CSV export that lives as an asset does not flood the chat with rows you never needed to see. Assets are how useful output survives without slowing the session down.
The kinds of assets a session can produce
Plexara asks the agent to pick the right shape of output for the job. An interactive dashboard is different from a narrative report is different from a CSV export is different from a single SVG chart. The agent gets these distinctions right when the prompt is explicit. The table below shows which asset type fits which kind of ask, with an example prompt for each.
The kinds of assets a session can produce
Interactive dashboard
Comparisons and exploratory views a teammate will want to click through. Filters, tabs, linked charts. HTML rendered inside the Plexara portal.
"Build an interactive dashboard comparing 2024 sales to 2025 by region and save it as Q1 regional comparison."
Markdown report
A narrative write-up with embedded numbers, charts, and conclusions. The right asset type for anything intended to be read top-to-bottom or shared with a non-technical reader.
"Draft a markdown report summarizing the Q3 numbers, highlighting the three stores with the biggest swings, and save it as a Q3 exec summary asset."
CSV / tabular export
Full-fidelity data for downstream tools: a spreadsheet, a BI tool, another Plexara session. Produced by trino_export when the row count would blow up the context window.
"Export the full 2025 transaction table filtered to the Southwest region as a CSV asset."
SVG chart
A single visualization suitable for embedding in a document or sharing as a self-contained image. Vector; scales cleanly.
"Create an SVG chart of revenue per store for the top ten stores by Q3 volume, saved as an asset."
Markdown document
Notes, methodology, caveats, anything text-shaped that is not a full report. Often created alongside other assets to document how they were built.
"Save a markdown document describing how the Q3 regional comparison was calculated, including the exact Trino queries used."
Collection
A grouping of related assets (dashboards, reports, exports, charts, markdown) into a single navigable briefing. Shareable as one link in the portal.
"Start a collection called Q1 regional review. Add the dashboard, the exec summary report, and the underlying CSV export."
Being explicit saves tokens and gets better outputs
Vague prompts ("generate a dashboard") lead to vague outputs. Worse, they leave the agent to decide whether to dump the output into the chat or save it as an asset, and the agent sometimes picks wrong. A prompt that names the asset type, the content, and the asset name routes the agent straight to the save_artifact tool.
A four-step workflow that works
The same rhythm covers almost every asset-producing session: preview the data quickly in the chat, then ask for the full deliverable as an asset, then iterate by reference, then gather related assets into a collection. Internalizing this pattern saves you from relearning it per session.
A four-step workflow that works across almost every asset request
- 01
Preview in the chat
Ask for a small sample first: "Show me a quick table of 2025 revenue by region so I can check the shape of the data." The goal is to confirm the agent has the right mental model and the numbers look roughly right before you commit to a larger deliverable.
- 02
Ask for the deliverable by type, and say "save as an asset"
Name the type explicitly (dashboard, report, CSV, SVG chart, markdown) and name the asset. This routes the agent straight to save_artifact instead of letting it dump HTML or rows into the chat.
- 03
Iterate by reference
When you want a change, say "open the Q1 regional comparison and add a chart showing year-over-year percent change." The agent calls manage_artifact and modifies the existing asset rather than regenerating from scratch.
- 04
Group related work into a collection
When a session produces several related assets, ask for them to be gathered into a named collection. That collection becomes a single shareable link to a briefing, not a chat transcript.
Collections: mini-portals for a topic
A single dashboard is useful. A collection of related assets (dashboard, report, export, methodology) is closer to how people actually do analytical work. Collections let the agent assemble a briefing during a session, with sections for the different pieces, and hand back a single shareable link at the end.
Where this leads
Assets are where useful output persists within the portal. Memory and the knowledge pipeline are what let the next session find those assets and remember what they were for. The next lesson covers both.
Key terms
Six terms cover the vocabulary you will see in asset-related tool results and any conversation about persisting session output.
Key Terms
- Assetartifact
- A persisted output produced during a Plexara session: a dashboard, report, CSV export, chart, markdown document, or similar. Lives in the Plexara portal, not in the conversation. Shareable and editable.
- Collection
- A grouping of related assets into a single navigable unit with sections. Shareable as one link. The closest Plexara analog to a briefing packet.
- save_artifact
- The tool the agent calls to create a new asset and store it in the portal. Returns a reference the agent shares back with the user instead of the full content.
- manage_artifact
- The tool the agent calls to edit an existing asset. Operates in place, keeping the asset identifier stable, so iterations do not create a pile of near-duplicates.
- trino_export
- A specialized write tool for the common case of “the query result is too big for the chat.” Runs the Trino query and writes the result as an asset (CSV, JSON, Markdown). Covered in detail in 204.
- Plexara portal
- The web UI where assets live. Users open, review, edit, and share assets here. The portal is also where administrators manage connections, personas, and the knowledge-review pipeline.