What is Agentic Analytics? How It Works, Examples

Data has always been a powerful tool for decision-making. But as businesses face increasing pressure to move faster and make smarter choices, the traditional dashboard-and-report model is starting to show its limits. Enter agentic analytics, a new approach that doesn’t just help you understand what happened, but actively works to help you determine what to do next.

With agentic analytics, autonomous AI agents become part of your team. These intelligent systems explore data on your behalf, uncover valuable information, and in many cases, take action, all without waiting for you to ask the right question. It changes the rules for anyone looking to make better, faster decisions, whether you’re a small business owner, a new data analyst, or a department leader managing complex initiatives.

Let’s break down how it works and what it looks like in practice.

The evolution of business intelligence

To appreciate what makes agentic analytics so powerful, it helps if we look back at how business intelligence has evolved.

1980s: Static reporting

Business intelligence began with manual reporting. Analysts compiled data in spreadsheets and distributed printed reports to executives. These reports were slow to produce and out of date as soon as they were printed.

1990s: Relational databases and OLAP

SQL-based querying and online analytical processing (OLAP) made it easier to access and slice structured data for multidimensional analysis.

2000s: Dashboards and self-service BI

Tools like Tableau and Qlik helped introduce visual dashboards and gave businesses the ability to explore data independently.

2010s: Augmented analytics

The rise of AI-enabled platforms introduced automated insights and anomaly detection. However, users still had to interpret results and take action.

2020s: Agentic analytics

Autonomous agents now proactively explore data, draw conclusions, and recommend or trigger actions in real-time.

This evolution marks a shift from data presentation to decision augmentation.

How agentic analytics works

At the core of agentic analytics are AI agents—goal-driven programs trained to independently perform tasks typically handled by analysts. But instead of simply following rules or responding to queries, these agents operate more like co-workers. They can learn from historical data, ask their own follow-up questions, experiment with different models, and iterate until they find something useful.

Here’s how a typical agentic analytics process works:

1. Goal setting: An analyst or business user defines a goal, such as “find reasons why customer churn is increasing” or “optimize marketing spend for Q3.”

2. Exploration and analysis: The AI agent ingests relevant data, analyzes patterns, tests hypotheses, and identifies contributing factors.

3. Iteration and action: Based on findings, the agent might suggest experiments, simulate outcomes, or trigger workflows to act on its insights, like flagging accounts for retention efforts or adjusting campaign budgets.

4. Human collaboration: While agents can operate independently, they are built to work alongside people. Humans stay in the loop to approve actions, ask follow-up questions, or refine the agent’s strategy.

Example walkthrough: Marketing campaign improvement

Imagine a mid-size retail company running multichannel digital marketing. A marketing agent is tasked with improving ROI from campaigns:

• The agent pulls in campaign data from Google Ads, Meta, and email platforms.
• It detects that CPC on Instagram is rising while conversions are falling.
• It tests budget reallocations toward higher-performing channels.
• It simulates the impact of doubling email frequency to engaged segments.
• The agent recommends a shift in budget and notifies the marketing lead.
• Upon approval, the agent applies the changes and continues monitoring.

Key components of an agentic system

• Data access: Agents must be securely connected to relevant data sources, including cloud apps, databases, and real-time feeds.
• Analytic logic: Many agents are built using large language models and machine learning to identify trends, clusters, or anomalies.
• Action layer: This layer connects agents to tools like CRM, marketing platforms, or workflow engines so they can act.
• Governance: Rules define what agents can and can’t do autonomously, maintaining oversight and control.

Traditional BI vs Agentic Analytics

To better understand the value of agentic analytics, it helps to compare it side-by-side with traditional business intelligence approaches. Traditional BI focuses on historical reporting and requires users to manually query data, generate reports, and interpret results. Agentic analytics, on the other hand, is designed to operate proactively. It uses autonomous agents to detect patterns, generate insights, and sometimes act without requiring a human to initiate every step. The table below outlines some of the key differences.

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Here’s how agentic analytics could work in the real world

To illustrate the potential of agentic analytics, here are a series of hypothetical examples showing how AI agents could support decision-making across different business functions. These scenarios are designed to reflect common roles and responsibilities, offering a glimpse into how agentic analytics could be applied in practice, no futuristic tech required.

Marketing optimization

A marketing team uses an agent to monitor campaign performance across channels. The agent reallocates budget automatically to the best-performing ads and flags underperformers for review.

Sales enablement

A sales agent analyzes CRM activity, surfaces warm leads based on recent engagement, and nudges reps with suggestions for next steps.

Finance forecasting

Finance teams use agentic tools to simulate different budget scenarios, detect anomalies in cash flow, and suggest cost-saving measures.

Customer experience

An agent monitors support tickets, detects a spike in issues around a new product feature, and alerts the product team before it escalates.

Operations

A supply chain agent identifies shipment delays, cross-references them with weather and vendor data, and proposes rerouting options.

HR analytics

An agent reviews employee sentiment survey results, turnover data, and team engagement metrics. It flags departments at risk of attrition and recommends interventions to HR leadership.

Agentic analytics for different roles

Agentic analytics isn’t just for technical teams or data scientists—it has practical, transformative value across roles. Whether you’re steering business strategy, managing a department, or just starting out in data, autonomous agents can help lighten your load, sharpen your insights, and amplify your impact. 

Here’s how agentic analytics can support different types of professionals in their daily work:

• Business owners: Use agents to monitor financial health, track key performance indicators, and receive alerts about anomalies without diving into spreadsheets.
• Department managers: Agents assist in resource planning, process optimization, and campaign tracking. They can suggest how to improve KPIs in real time.
• New data analysts: Get support from agents that handle data exploration, generate visualizations, and even suggest statistical techniques. Analysts can learn while working with agents instead of starting from scratch.

Technical foundations behind the scenes

Agentic analytics draws from a combination of advanced technologies that work together to make autonomous data analysis and decision-making possible. While each component plays a distinct role, their integration is what makes these systems intelligent, responsive, and practical for real-world use.

• Large language models (LLMs): These AI models interpret natural language inputs and generate human-like responses. In agentic analytics, LLMs help agents understand user goals, generate queries, and summarize insights in plain language.
• Machine learning algorithms: These are responsible for detecting patterns, forecasting trends, and identifying anomalies. They allows agents to move beyond descriptive reporting to deliver predictive and prescriptive insights.
• Workflow automation tools: Automation frameworks allow agents to take action based on insights. For instance, they can reallocate budgets, send alerts, update CRM records, or launch new processes—all without manual intervention.
• Vector databases: These databases store unstructured and semi-structured data—like text, images, or logs—in a way that introduces semantic search and contextual understanding. They’re key for enabling LLMs to retrieve relevant context efficiently.
• APIs and data connectors: These serve as the glue between disparate systems. APIs and connectors let agents pull in data from third-party platforms, write back updates, and easily integrate with cloud apps and enterprise systems.
• Orchestration and monitoring layers: These oversee agent behavior, ensure agents follow defined governance protocols, and monitor performance for accuracy and compliance.

These technologies converge to create agentic systems that are not only intelligent but also reliable and scalable. The result is a data environment where AI agents can continuously learn, adapt, and contribute to business outcomes with minimal friction. to create systems that are flexible, scalable, and increasingly user-friendly.

How to implement agentic analytics

Agentic analytics isn’t just for large enterprises or tech companies. Here’s how any organization can start:

1. Identify a high-impact use case: Focus on a problem where faster decisions could add real value.
2. Prepare your data: Ensure your data is clean, connected, and accessible to AI systems.
3. Choose a platform: Use a BI platform that supports agentic workflows and integrates with your systems (like Domo AI).
4. Evaluate tools: Look for platforms with intuitive interfaces, explainability features, integration flexibility, and strong governance.
5. Involve the right team: Include data engineers, analysts, and business stakeholders early on.
6. Set governance rules: Define what agents can recommend versus what they can execute.
7. Pilot and iterate: Start small in one department or workflow and adjust based on feedback.

Common challenges (and how to address them)

While the promise of agentic analytics is compelling, it’s important to approach implementation with a realistic understanding of potential pitfalls. Like any emerging technology, agentic systems come with challenges that range from data integrity to user trust. Addressing these early—and proactively—can make all the difference in successful adoption and long-term value.

Data quality

Garbage in, garbage out. Establish strong data pipelines and validation processes.

Change management

People may be skeptical about AI making decisions. Start with collaborative agents that assist, not replace. Use training and early wins to build confidence.

Bias and fairness

Agents reflect the data they’re trained on. Use diverse data sets and conduct regular audits of recommendations.

Transparency

Ensure agents can explain their reasoning, especially for high-impact decisions. Consider using explainable AI (XAI) frameworks.

Ethical considerations

Avoid automating sensitive decisions without human oversight. Set clear boundaries on what agents can and can’t do.

The future of agentic analytics

Agentic analytics is just getting started. In the near future, we’ll likely see:

• Agent-to-agent collaboration: Multiple agents coordinating tasks across departments.
• Low-code agent builders: Business users creating their own agents without technical help.
• Integrated workflows: Agents embedded directly into tools like Slack, email, or CRM.
• Regulatory frameworks: Standards to govern agentic behavior in sensitive industries.
• Industry-specific agent templates: Pretrained agents tailored to roles in finance, marketing, HR, and operations.

Agentic analytics transforms how we work with data. It doesn’t just help us understand the past—it actively helps shape the future. By bringing proactive, intelligent agents into our decision-making processes, we can move faster, act smarter, and uncover opportunities we never saw coming.

Bring agentic analytics to life with Domo

With Domo AI, agentic analytics isn’t just a concept—it’s a capability you can start using today. Domo’s AI Service Layer enables intelligent agents that connect directly to your data, analyze trends, suggest actions, and even execute workflows. Whether you’re monitoring financial performance, optimizing marketing spend, or spotting operational issues before they escalate, Domo’s AI agents are designed to help your team move faster with confidence.

If you’re ready to take your data strategy to the next level, Domo makes it easy to get started. Explore how our agentic capabilities can work within your existing tech stack and scale with your business goals.

Start your journey with Domo AI today and let intelligent agents help you make smarter decisions, faster. It doesn’t just help us understand the past—it actively helps shape the future. By bringing proactive, intelligent agents into our decision-making processes, we can move faster, act smarter, and uncover opportunities we never saw coming.

If you’re ready to take your data strategy to the next level, agentic analytics is a great place to start.