The evolution of large language models (LLMs) has revolutionized many fields, including analytics. Traditionally, LLMs have been integrated into analytics workflows to assist in explaining data, generating summaries, and uncovering insights. However, a more recent breakthrough in AI, Agentic AI which involves the development of AI systems, composed of multiple agents, each with a defined purpose, capable of autonomous decision-making and self-directed actions.
This shift is now making its way into the analytics domain, transforming how we interact with data. According to Gartner:
By 2026, over 80% of business consumers will prefer intelligent assistants and embedded analytics over dashboards for data-driven insights.
Agentic AI is reshaping the analytics landscape by enabling conversational, intelligent, and proactive data experiences.
In this blog, we’ll explore how agentic analytics is redefining data workflows and making data-driven decision-making more accessible, intelligent, and efficient.
What is Agentic Analytics?
In the realm of data analytics, driving insights is often a complex and time-consuming process. Data professionals invest significant effort in preparing the right data, cleaning and organizing it, and finally reaching meaningful conclusions. With the rise of LLM-powered agents, many of these tasks have become easier and more efficient.
Today, different types of agents can be employed at various stages of the analytics lifecycle. When these agents are granted autonomy and integrated across the entire analytics workflow, they form a cohesive, intelligent system known as Agentic Analytics. This paradigm shift enables more conversational, dynamic, and accessible ways to work with data.
Why Shift to Agentic Analytics?
How Does Agentic Analytics Differ?
To better understand the impact of Agentic Analytics, let’s compare it with traditional business intelligence approaches and AI-assisted methods:
How It Works: Components of Agentic Analytics
Agentic Analytics brings together Agentic AI and data analytics to turn raw business data into intelligent, actionable insights. To achieve this, it builds on the core architectural components of Agentic AI, enhanced with analytics-specific modules. Let’s break down some key components:
1. AI Agents (LLM-Powered)
At the core of Agent Analytics are autonomous AI agents, powered by large language models (LLMs). These agents can:
Access and query data sources
Interpret user intent
Generate automated insights and summaries
Take actions like triggering alerts, or recommending decisions
2. Memory and Learning Module
This component stores user preferences, like frequently asked questions, preferred data formats, past interactions, and recurring topics. By leveraging this memory, the system personalizes future responses and learns over time, leading to smarter, more relevant interactions.
3. Semantic Module
The semantic layer is foundational to both analytics and agentic AI. It serves as a unified interface that bridges the gap between raw data and business context, adding business logic, key metrics, governance, and consistency to raw data, ensuring that insights are not only accurate but also aligned with the organization’s definitions and standards.
4. Data Sources & Tools Integration
Agentic Analytics systems must connect to a wide variety of data sources and tools that agents can access to perform their tasks. These include structured databases, analytics tools, ETL tools, business applications, etc.
Agentic Analytics systems are powered by a collection of specialized autonomous agents, each with a clear role in the analytics lifecycle. Let’s have a look at some fundamental agents involved in analytics:
1. Planner Agent
Acts as the strategist. Breaks down a business request into smaller analytical tasks, assigns them to the right agents, and manages the execution to ensure goals are met efficiently.
Example:
A business launched a new smartwatch, and now the project manager needs a report to “assess sales, engagement, and market reception.” The Planner Agent interprets the goal and creates a multi-step workflow and delegates tasks to the appropriate Agents.
2. Data Agent
Acts as the data connector. Identifies the right data sources, retrieves relevant datasets, and ensures secure, accurate access to information across internal systems and external APIs.
Example:
The Data Agent pulls sales data from the ERP, website analytics from Google Analytics, customer reviews from e-commerce platforms, and social media mentions via APIs.
3. Data Preparation Agent
Acts as the data wrangler. Cleans, transforms, and enriches datasets so they are ready for analysis. Handles formatting, joins, missing values, and data consistency checks.
The Prep Agent merges sales and marketing data, enriches customer profiles with demographic details, and prepares engagement metrics for further analysis.
4. Analysis Agent
Acts as the analyst. Selects and applies the appropriate analytical or statistical methods to uncover patterns, trends, and correlations in the data by generating code or SQL queries.
Example:
The Analysis Agent calculates units sold per region, tracks repeat purchase rates, compares previous launch sales with new ones, identifies the most effective marketing campaigns, and detects patterns.
5. Visualization Agent
Acts as the storyteller. Generates visuals, charts, and tables that make complex data easy to understand for different stakeholders.
Example:
The Visualization Agent builds interactive dashboards showing sales heatmaps, engagement trends over time, and customer sentiment charts.
6. Monitoring Agent
Acts as the supervisor. Monitors results from all agents and ensures actions are initiated when needed.
Example:
The agent coordinates with other agents, monitors sales, and sets up real-time alerts for sentiment drops or sales spikes.
Real-World Examples of Agentic Analytics Platforms
Tableau Next
Tableau Next is Salesforce’s next-generation agentic analytics platform, tightly integrated with Agentforce, Salesforce’s digital labor framework. Its data foundation ensures enterprise-grade security, compliance, and agility while unifying customer data for holistic analysis.
Built as an open, API-first platform, Tableau Next offers reusable, discoverable analytic assets and a flexible architecture to meet evolving business needs. By embedding AI-powered insights directly into workflows, it allows decision-makers to act on relevant, real-time intelligence without switching tools, making insight delivery truly seamless.
source: Tableau
ThoughtSpot
ThoughtSpot delivers fast, accurate AI-driven insights through a unified platform powered by AI agents, connected insights, and smart applications. It streamlines the entire analytics lifecycle from data connection, exploration, and action into a single, cohesive environment.
Unlike traditional BI tools that require users to log into dashboards and search for answers, it allows organizations to integrate analytics into custom apps and workflows effortlessly. Every AI-generated insight is fully transparent, with the ability to verify results through natural language tokens or SQL queries, ensuring trust, governance, and AI explainability.
source: Thoughtspot
Tellius
Tellius combines dynamicAI agents with conversational intelligence to make analytics accessible to everyone.
The platform integrates data from multiple systems into a secure, unified knowledge layer, eliminating silos and creating a single source of truth. Multi-agent workflows handle tasks such as planning, data preparation, insight generation, and visualization. These agents operate proactively, delivering anomaly detection, segmentation, root-cause analysis, and actionable recommendations in real time.
While agentic analytics offers tremendous potential, realizing its benefits requires addressing several practical and strategic challenges:
Data Quality and Integration
Even the most sophisticated AI agents are limited by the quality of the data they consume. Siloed, inconsistent, or incomplete data can severely degrade output accuracy. To mitigate this, organizations should prioritize integrating curated datasets and implementing a semantic layer, offering a unified and consolidated view across the organization.
Cost Management
Autonomous AI agents often operate in a continuous listening mode, constantly ingesting data and running analysis, causing high token consumption and operational cost. Techniques like Agentic Retrieval-Augmented Generation (RAG) and context filtering can reduce unnecessary data queries and optimize cost efficiency.
Trust and Transparency
Building trust, transparency, and explainability in agentic systems becomes fundamental as users are made to rely on AI-driven decisions. Incorporating transparent decision logs, natural language explanations and clear traceability back to source data and agentic flow help users not only verify results but also understand the process of their generation.
Security and Compliance
When AI agents are given autonomy to pull, process, and act on enterprise data, strict access control and compliance safeguards are essential. This includes role-based data access, data masking for sensitive fields, and audit trails for agent actions. It also involves ensuring agent operations align with industry-specific regulations such as GDPR or HIPAA.
Response Quality
AI agents have the tendency to produce responses that differ from business logic, raising concerns about their use in decision-making. To address this, a clear orchestration framework with well-defined agents is essential. Other strategies include adding a semantic layer for consistent business definitions and a reinforcement learning layer to enable learning from past feedback.
Agentic analytics represents an evolution in the analytics landscape where insights are no longer just discovered but are contextual, conversational, and actionable. With Agentic AI, insights are described, root cause is diagnosed, outcomes are predicted, and corrective actions are prescribed, all autonomously.
To unlock this potential, organizations must implement an agentic system, ensuring transparency, maintaining security and governance, aligning with business requirements, and leveraging curated, trusted data.
According to Gartner, augmented analytics capabilities will evolve into autonomous analytics platforms by 2027, with 75% of analytics content leveraging GenAI for enhanced contextual intelligence. Organizations must prepare today to lead tomorrow, harnessing what, why, and how of data in a fully automated, intelligent way.
