Root: Context Agent

Your AI data team member that builds and maintains your knowledge base

Root is an AI agent that helps you build, maintain, and evolve your organization's knowledge base. It runs in an isolated sandbox with access to your connected tools—databases, BI dashboards, and past conversations—and can create documentation automatically.

Root helps you curate and share company knowledge with Dot

Why this matters: Building a comprehensive knowledge base manually takes months. Root accelerates this by extracting business logic from your existing systems and learning from how your team actually uses data.

Root also learns on its own. When Dot spots something worth remembering during a conversation — a corrected metric, a renamed table, a missing definition — it sends a proposal to Root. An admin reviews it, and if it looks right, merges it with one click. Dot gets smarter every day, and humans stay in control.

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Workflow

1. Open Context Agent from the sidebar

2. Describe what you need in natural language—Root understands complex requests

3. Approve tool use when Root needs to query data or make changes

Review and approve changes before they're applied

1. Review the diff to see exactly what changed

Click Review Changes to see pending modifications

1. Merge to production when satisfied—or discard and try again

Review the diff and merge when ready

All changes happen in an isolated sandbox. Nothing goes live until you explicitly merge.

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Use Cases

1. Extract Metrics from BI Tools

Problem: Your Tableau/Metabase dashboards contain business logic, but it's not documented anywhere Dot can use.

Solution: Give Root access to your most trusted dashboards and ask it to create a metric glossary.

Here are our five most trusted Metabase dashboards—analyze them and
create a glossary of key metrics with their definitions.

Root will:

• Connect to your BI tool via API

• Extract calculations, filters, and business logic

• Create standardized metric definitions Dot can use

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2. Learn from Past Conversations

Problem: You don't know what questions your team asks most or what's missing from your documentation.

Solution: Ask Root to analyze past Dot conversations.

Analyze the last 30 days of conversations. What are the most
common questions? Are there patterns in failed queries?

Root will:

• Export and analyze conversation history

• Identify frequently asked questions

• Find gaps where Dot couldn't answer

• Suggest documentation improvements

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3. Audit Existing Documentation

Problem: Your table descriptions were written months ago. Are they still accurate?

Solution: Ask Root to find inconsistencies.

Are there inconsistencies in our documentation or data source
descriptions? Check if sample values match descriptions.

Root analyzes your context and identifies inconsistencies

Root will:

• Read your current documentation

• Query actual data to verify descriptions

• Flag mismatches between docs and reality

• Suggest fixes

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4. Interview-Based Knowledge Capture

Problem: Tribal knowledge exists in people's heads, not in documentation.

Solution: Let Root interview domain experts and capture their knowledge.

Interview me about how we handle sales and create a note.

Root will:

• Ask targeted questions about your process

• Capture answers in structured notes

• Create documentation that reflects actual practice

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5. Bulk Table Documentation

Problem: You have hundreds of tables but no descriptions.

Solution: Point Root at your schema and let it document everything.

Activate all tables in the 'REPORTING' schema. Add descriptions
based on column names and sample values.

Root will:

• Query database metadata

• Analyze column names, types, and sample data

• Generate descriptions for each table and column

• Save as documentation Dot can use

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6. Migrate Documentation

Problem: Your documentation lives in Confluence/Notion, not where Dot can use it.

Solution: Ask Root to migrate it.

Here's a link to our Confluence space. Extract the key business
definitions and create notes for Dot.

Root will:

• Fetch content from external sources

• Extract relevant business context

• Create notes in Dot's format

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7. "Remember This"

Problem: Someone on your team knows that "fiscal year starts in April" or that the orders table was renamed to transactions last month — but that knowledge is stuck in their head.

Solution: During any conversation, just tell Dot to remember it.

Remember that our fiscal year starts in April, not January.

Dot will propose a knowledge base update. An admin sees the proposal, reviews what changed, and merges it — or rejects it if something looks off.

The people closest to the data are the ones who catch mistakes first. This lets them fix things on the spot, with an admin verifying before it goes live.

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8. Investigate a Chat

Problem: A user had a bad experience — wrong numbers, a confusing chart, or a query that missed the point. You want to know why.

Solution: Open the chat from your history and click Investigate. Root reads everything Dot did during that conversation — which tables it picked, which SQL it wrote, where it went wrong — and explains the root cause.

Look into this chat and make sure this mistake doesn't happen again.

Root will:

• Trace every decision Dot made in that conversation

• Identify where things went wrong

• Propose a fix — a corrected note, a missing relationship, or a clearer description

Instead of manually debugging, you get a diagnosis and a fix in one step.

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9. Find Recurring Issues

Problem: The same type of mistake keeps happening across different users and conversations, but nobody has connected the dots.

Solution: Ask Root to look for patterns.

What are the most common errors across last week's conversations?
Suggest fixes for the top three.

Root will:

• Scan recent conversations for recurring failures

• Group them by root cause

• Propose targeted knowledge base improvements for each

This turns reactive troubleshooting into proactive improvement.

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How Dot Learns

Dot improves its knowledge base continuously, but nothing changes without admin approval:

1. Dot spots something — during a conversation, Dot notices a mismatch, or a user explicitly says "remember this"

2. A proposal appears — in Root's history, admins see pending proposals with a clear diff of what would change

3. Admin reviews — open the proposal, see exactly what's being added or corrected, and why

4. Merge or reject — one click to approve, or reject if it's not right

5. Dot is smarter — the improvement is live immediately for all users

Dot suggests. Humans decide.

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How It Works

1. Start a session from the sidebar (Context Agent)

2. Ask Root what you need—it understands natural language

3. Review changes before they go live (git-based versioning)

4. Merge to production when you're satisfied

All changes are version-controlled. You can pause, resume, or discard work at any time.

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What Root Can Access

Source	Capability
Databases	Execute SELECT queries, analyze structure
BI Tools	Read Tableau/Metabase dashboards via API
Past Conversations	Analyze Dot usage patterns
Conversation Traces	Replay and diagnose any past Dot conversation
Web	Search for documentation and best practices
Your Notes	Read and edit existing documentation

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Tips

• Start specific: "Document the orders table" works better than "document everything"

• Iterate: Root can refine its work—ask for changes if the first draft isn't right

• Review diffs: Always review changes before merging to production

• Use interviews: For complex processes, let Root interview you rather than trying to explain everything upfront

• Review proposals regularly: Dot learns fastest when proposals are reviewed quickly

• Encourage "remember this": The people closest to the data catch the best corrections — let them contribute

• Investigate disliked chats: The fastest way to improve Dot is to diagnose what went wrong and fix it at the source