Page 5 — Prompt Flow & Agents

The Prompt Flow page is the control center for customizing how Intugle's AI agents think, reason, and respond to your questions. This is where you fine-tune the AI to understand your specific business context, terminology, and data.

Admin access required

To edit prompts, concepts, few-shots, and metadata, you must enable the Admin toggle (top-right corner). Without it, you can view but not modify configurations.

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Understanding the Prompt Flow System

When you ask Intugle a question, here's what happens behind the scenes:

┌─────────────────────────────────────────────────────────────────────┐
│                        YOUR QUESTION                                 │
│              "What were our top products last quarter?"              │
└─────────────────────────────────────────────────────────────────────┘
                                  │
                                  ▼
┌─────────────────────────────────────────────────────────────────────┐
│                    ORCHESTRATION AGENT                               │
│  • Reads your question                                               │
│  • Checks Universal Instructions (global rules)                      │
│  • Decides which specialist agent(s) to call                         │
└─────────────────────────────────────────────────────────────────────┘
                                  │
                    ┌─────────────┼─────────────┐
                    ▼             ▼             ▼
           ┌───────────┐  ┌───────────┐  ┌───────────┐
           │ MetaData  │  │   Data    │  │ Semantic  │
           │ Analysis  │  │ Analysis  │  │   Graph   │
           └───────────┘  └───────────┘  └───────────┘
                    │             │             │
                    └─────────────┼─────────────┘
                                  ▼
┌─────────────────────────────────────────────────────────────────────┐
│                    AI REFERENCES:                                    │
│  • Analytics Catalog → Business concept definitions                  │
│  • Few-Shots → Example Q&A pairs for similar questions               │
│  • Metadata → Table/column descriptions and relationships            │
└─────────────────────────────────────────────────────────────────────┘
                                  │
                                  ▼
┌─────────────────────────────────────────────────────────────────────┐
│                        YOUR ANSWER                                   │
│  "Based on your sales data, the top 5 products by revenue were..."   │
└─────────────────────────────────────────────────────────────────────┘

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Page Layout

The page has four tabs across the top:

Tab	Purpose
Agent Configurator	Edit the prompts that control how each AI agent behaves
Few-Shots	Create example Q&A pairs to train the AI
Analytics Catalog	Define business concepts and metric formulas
Metadata	View and edit table/column descriptions

Global Controls (Top-Right)

Control	What It Does
Subscription toggle	Switch between subscription views
Admin toggle	Enable edit mode for all configurations
+ Add LLM Model	Connect additional AI models (GPT-4, Claude, Gemini, etc.)

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Tab 1 — Agent Configurator

The Agent Configurator lets you customize the system prompts that control how each AI agent processes your questions.

Three-Panel Layout

Panel	What It Shows
Left: Categories	List of all AI agent types
Center: Subcategories & Tasks	The specific tasks within each agent
Right: Prompt Editor	View/edit the actual prompt text

Agent Categories Explained

Each agent is a specialist that handles a specific type of task:

Agent	Role	When It's Called
Orchestration Agent	The "manager" — decides which specialist to call	Every question goes through this first
Semantic Graph Agent	Navigates table relationships	When questions involve multiple related tables
MetaData Analysis	Understands table structure	When the AI needs to know what columns exist
Data Analysis	Writes and runs SQL queries	When you ask for specific data or metrics
Data Transformation	Cleans and reshapes data	When data needs formatting or aggregation
Link Discovery	Finds hidden relationships	When discovering new table connections
Document Search	Searches uploaded documents	When questions relate to PDFs, Word docs, etc.
Web Search	Searches the internet	When contextual web information is needed

How to Edit an Agent Prompt

1. Click an agent category (left panel)
2. Select a subcategory (center panel)
3. Choose a task (center panel)
4. Edit the prompt in the Prompt Editor (right panel)
5. Click Save — changes take effect immediately

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Tab 2 — Few-Shots

Few-Shots are example question-and-answer pairs that teach the AI how to handle specific types of queries for your business.

Why Few-Shots Matter

Without few-shots, the AI might:

• Use the wrong table for a query
• Misunderstand business terminology
• Calculate metrics incorrectly

With few-shots, you show the AI: "When someone asks THIS, here's exactly how to answer."

Few-Shot Card Details

Each card shows:

Field	Description
Name	Descriptive title (e.g., "Customer 360 Query")
Status	🟢 Active (AI uses it) or 🔴 Inactive (disabled)
Table Tags	Which data tables this example references
Created By	Who authored this few-shot

Top-Right Actions

Button	What It Does
Find Similar	Identifies duplicate or overlapping few-shots
Import/Export	Bulk import/export few-shots as JSON or CSV
Run All	Tests all active few-shots to verify they still work
+ New Few-Shot	Create a new example Q&A pair

Creating a New Few-Shot

1. Click + New Few-Shot
2. Enter a question — the type of query users might ask
3. Enter the ideal answer or SQL query the AI should generate
4. Select which tables are involved
5. Set status to Active
6. Click Save

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Tab 3 — Analytics Catalog

The Analytics Catalog is a library of business concepts and metric definitions that the AI references when answering questions.

What is a Concept?

A concept is a business term with a defined meaning. For example:

Concept Name	Definition
Customer Churn	"Customers who cancelled their subscription in the last 30 days, calculated as cancelled / total active"
Net Revenue Retention	"Revenue from existing customers this period / revenue from same customers last period × 100"
Top Products	"Products ranked by total order value, excluding returns and refunds"

Why the Catalog Matters

Without catalog entries, asking "What's our churn rate?" might give inconsistent results because the AI doesn't know YOUR specific definition. With a catalog entry, it always uses your formula.

Top-Right Actions

Button	What It Does
Universal Instructions	Set global rules that apply to ALL concepts and AI responses
Find Similar	Find duplicate or overlapping concepts
Import/Export	Bulk import/export concepts
+ New Concept	Create a new business concept definition

Universal Instructions

Click Universal Instructions to open a dialog where you can set global rules that apply to ALL AI interactions:

Field	Description
Instruction Content	Global rules the AI follows for every query (e.g., "Always report currency in USD", "Use fiscal year starting April 1")
Change Note	Optional description of what you changed (for version history)
Version History tab	View previous versions of the instructions

Creating a New Concept

1. Click + New Concept
2. Enter a concept name (e.g., "Monthly Active Users")
3. Write a description explaining what this metric means and how to calculate it
4. Set status to Active
5. Click Save

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Tab 4 — Metadata

The Metadata tab provides a spreadsheet-like view of all your data tables, columns, relationships, and descriptions.

Page Header Summary

Metadata Management
Excel-like view for all column metadata and table relationships
19 tables · 192 columns · 35 links

This tells you exactly how much data the AI has access to.

Three Sub-Tabs

Sub-Tab	What It Shows
Table Metadata	All tables with domain, name, description, row/column counts
Column Metadata	Every column across all tables with types and descriptions
Table Relationships	Foreign-key links between tables

Table Metadata Columns

Column	Description
Domain	Business domain (Marketing, Sales, Products, Support)
Table Name	The actual table name in your database
Glossary	AI-generated description of what the table contains
Table Type	Source = raw data table
Row Count	Number of rows
Column Count	Number of columns

Top-Right Actions

Button	What It Does
Usecase Summary	View a summary of use cases across your data
History	See change history of metadata edits
Commit Changes	Save and apply any edits you've made
Profile dropdown	Switch between metadata profile views
Columns dropdown	Show/hide columns in the grid

Editing Metadata

Click any cell to edit it directly:

• Edit Glossary descriptions to help the AI understand table contents
• Change Domain assignments to re-classify tables

warning

Click Commit Changes (red button) after editing — changes are NOT saved automatically.

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How It All Works Together

Here's a complete example of how Prompt Flow components work together:

Scenario: User asks "What's our customer churn this quarter?"

1. Orchestration Agent receives the question
2. It checks Universal Instructions for global rules
3. It routes to MetaData Analysis to find relevant tables
4. MetaData Analysis checks the Metadata tab and finds customers, subscriptions tables
5. The system checks the Analytics Catalog and finds a "Customer Churn" concept definition
6. It checks Few-Shots for similar questions that have been answered before
7. Data Analysis agent writes a SQL query using:
   • The concept definition (how to calculate churn)
   • The metadata (which tables/columns to use)
   • Any matching few-shot examples
8. The query runs and returns results
9. The answer is formatted and returned to the user

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Best Practices

For Agent Prompts

• Keep prompts concise and specific
• Include examples of expected behavior
• Test changes with real questions before deploying

For Few-Shots

• Create few-shots for your most common questions
• Include variations of how users might ask the same thing
• Run "Run All" periodically to catch broken examples

For Analytics Catalog

• Define ALL your key business metrics
• Be specific about calculation methods
• Include edge cases (e.g., "exclude returns from revenue calculations")

For Metadata

• Write clear, business-friendly descriptions for every table
• Keep descriptions up-to-date when schemas change
• Use consistent domain classifications