Agentic System for Conversational Data Querying and Visualisation

Introduction

Imagine asking a complex business question like “What was our monthly revenue trend over the past year for high-value customers?”—and getting an insightful graph in response through simple conversation.

Welcome to Agentic Query & Visualisation Systems—AI-powered assistants enabling seamless, natural language interaction with data systems. These systems transform how users access, explore, and visualize data by combining intent recognition, task-specific agents, and real-time analytics.

This article outlines a modular architecture for designing such systems, focusing on conversational interfaces supporting multi-intent queries, dynamic agent orchestration, and secure, real-time data interaction.

System Overview

The Agentic Query & Visualisation System acts as an intelligent assistant understanding user queries, identifying intent behind them, and orchestrating various specialized agents to complete tasks. Whether users want data reports, troubleshooting support, or general interaction, the system responds with appropriate tools, insights, or visuals.

System Design Goals

• Multi-intent recognition
• Natural language understanding
• Secure database interaction
• Real-time response generation
• Visual output rendering

Key Architectural Components

1. Entry Points

The system supports multiple user interaction modes:

• A web interface offering modern chat-like experience with visual output
• A CLI interface for quick tests, scripting, or developer-focused automation

2. Processing Pipeline

The system follows a structured flow:

1. User sends a message (text-based query)
2. System analyzes the message to determine intent
3. Based on detected intent, routes task to appropriate specialized agent
4. Selected agent processes query and prepares result (e.g., data visualisation, natural language answer)
5. Response is formatted and presented back to user

3. Agents: Task-Specific Intelligence Units

Agents are purpose-built units responsible for executing specific task types. These modular agents can be plugged in or extended easily.

Query Agent

Most advanced agent, tasked with translating natural language questions into executable database queries. Leverages schema-aware toolset ensuring accuracy and relevance.

Key responsibilities:

• Parsing complex, multi-intent, or nested queries from user input
• Understanding temporal expressions and aggregate concepts like “last quarter” or “average revenue”
• Generating structured queries aligned with current database schema
• Validating queries for completeness, correctness, and safety
• Executing queries and retrieving corresponding data results

Visualisation Agent

Once data queries execute and results are retrieved, the Visualisation Agent transforms results into meaningful visual outputs. Selects most appropriate chart or graph type (bar chart, line graph, pie chart) based on data structure and semantics.

Capabilities:

• Automatically determining optimal visualization format based on query’s aggregation or comparison needs
• Supporting multiple chart types such as time-series trends, category breakdowns, and comparison plots
• Generating plots using libraries like Plotly for seamless UI integration
• Enhancing user experience by summarizing visuals in plain language

Example: If a user asks “Compare monthly revenue by product category for the last 6 months,” the agent generates a grouped bar chart showing revenue trends for each category across the requested timeframe, along with summary insights.

Conversation Agent

General-purpose dialogue agent managing casual or off-topic conversation. Enhances user engagement and keeps interaction smooth between tasks.

Help Agent

Guides users in using the system effectively. Provides onboarding, feature walkthroughs, and answers to “How do I…” style questions.

Tech Support Agent

Handles troubleshooting for system issues, helping users resolve errors or raise support tickets when queries aren’t answered.

Each agent operates under a clear persona, has defined skills, and reasons within its scope. The orchestrator ensures the right agent is called at the right time.

Query Toolchain: The Execution Backbone

The system includes powerful tools augmenting agent capabilities, particularly the Query Agent. These tools handle parsing, validation, optimization, and execution of queries, ensuring accuracy, safety, and efficiency.

Key Tools

Multi-Query Parser Tool

Deconstructs compound or multi-intent queries into smaller, manageable units. For example: “Show me sales in Q1 and list top-performing regions” gets split into two parallel queries.

Query Validator Tool

Ensures user queries contain enough concrete details—like dates, timeframes, codes, statuses, or quantities—to translate into valid and precise database queries. Uses schema and contextual time awareness to validate user intent, reword ambiguous queries, or stop incomplete ones.

Examples:

• Vague query: “Show me sales for last month” → Transformed: “Show me sales for the period 01-May-2025 to 31-May-2025”
• Incomplete query: “Get report for region” → Flagged for missing details; system prompts user to specify which region and what type of report

Query Generator Tool

Translates natural language into query language syntax (e.g., SQL or MongoDB queries). Uses both templates and LLM-assisted techniques ensuring accuracy and flexibility.

Query Analyser Tool

Acts as security and compliance layer. Checks for:

• Schema consistency
• Access (Read/Write/Delete) violations
• Query injection patterns
• Query complexity limits

Query Executor Tool

Connects securely to database, runs validated queries, and fetches results in real-time. Supports pagination, limits, and result formatting.

These tools work together enabling natural language interfaces to run powerful, production-grade data queries with speed and confidence.

Schema Awareness

A dynamic Schema Manager continuously parses connected database schema to understand:

• Collections or tables
• Field types and relationships
• Existing indexes

This enables the system to:

• Provide context-aware query generation
• Offer intelligent auto-complete
• Prevent malformed or unsupported queries

Data Flow in Action

A typical interaction:

1. User types: “Compare total sales across all product categories for the past 6 months.”
2. System classifies message as DATABASE_QUERY intent
3. Master Orchestrator invokes Query Agent
4. Agent uses Parser, Analyser, Validator, and Generator tools to prepare query
5. Executor runs it and returns results
6. Visualisation agent analyzes result and generates suitable visualization (e.g., grouped bar chart)
7. Response is sent back to UI in natural language + chart form

Security & Reliability

To ensure trust and reliability, the system includes:

• Validation layers for every query to prevent data leakage or attacks
• Intent fallback mechanisms to avoid breaking conversation
• Role-based access controls governing what each user can see or do
• Structured logging and alerts for monitoring and debugging

Final Thoughts

Agentic Query & Visualisation Systems represent a shift from static dashboards to dynamic, human-centric data interaction. By combining modular agents, intelligent tooling, and natural conversation, these systems empower non-technical users to explore and understand complex data with ease.

As enterprises move toward data democratization, the need for such AI-powered systems will only grow. They are not just technical enhancements—they are productivity revolutions.