Conversational Analytics: Best Practices for AI Agents

Conversational analytics is changing how enterprise users interact with data, making insights more accessible, intuitive, and real-time. Users can now communicate with data through natural language conversations instead of static dashboards, SQL queries, or engaging technical teams. Powered by generative AI (GenAI) and multi-agent systems, conversational analytics turns questions into actionable insights.

This article focuses on the types of AI agents that power conversational analytics and how they translate insights into action across areas like customer support, supply chain management, financial planning, healthcare, and retail analysis. By the end of the article, you will understand the best practices that apply to AI-assisted platforms enabling conversational analytics across multiple data sources ranging from traditional databases to unstructured text and third-party applications.

Summary of key concepts in conversational analytics

The table below summarizes key conversational analytics topics this article explores in detail.

What is conversational analytics?

Conversational analytics is a business intelligence methodology that helps users interact with data using natural language through chat-based interfaces or voice-based systems.

It builds on the principles of self-service analytics and self-service BI, and aims to make data access and analysis intuitive, immediate, and accessible for everyone.

Historically, analytics required creating a dashboard, writing SQL queries, or going through several reporting tools to ask questions such as "What were the top-selling products last month in the Northeast region?"

With conversational analytics, that same question can be typed (or spoken) in plain English, and the system returns a direct, data-driven response often accompanied by visualizations or recommendations. This approach dramatically reduces dependency on technical teams and empowers business users to explore data on their own terms.

Conversational analytics relies on GenAI. Specifically, the popular large language models (LLMs) that power many modern GenAI use cases. Conversational analytics includes capabilities like AI-powered SQL translation to translate natural language inputs into structured SQL queries. For example, an operations manager does not need to learn SQL syntax and instead just types:

“Show the average delivery time by carrier for the past month.”

In the background, the AI system translates this to a query, retrieves the required data, and returns a clear response, often accompanied by visualizations. Some enterprise solutions, like WisdomAI, take this further by combining LLMs with metadata layers and business context, enabling users to access expert-level insights from enterprise data without hassle.

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Role of metadata in conversational analytics

Metadata is critical for conversational analytics systems to interpret user’s queries correctly. Early LLM-based systems provided the table schema in the prompt. One of the main issues with this approach is selecting the correct table schemas to include in the prompt, which requires knowledge from the analyst. Even with longer LLM context windows, including the complete database schema with every prompt becomes inefficient and introduces further challenges because this approach relies on the LLM fully understanding the database schema of the user's natural language query. The LLM would need to disambiguate the meaning of terms used within your organization to select the correct tables/columns and deduplicate where multiple tables/columns exist.

Instead, a semantic/context layer enables relationships between tables/columns and business-specific terminology to be captured and used to ground the LLM. The importance of this metadata is explored further in our AI Metadata article.  

Role of AI agents in conversational analytics

AI agents are modular pieces that conduct operations in each stage of the analytics process. These agents work behind the scenes in conversational analytics systems to decode user queries, capture all relevant data, synthesize insights, and trigger actions. Each AI agent is designed to handle a specific task, and together, they form a coordinated system that turns natural language input into data-driven decision-making.

This multi-agent setup allows users to ask questions in natural language and have the system respond with answers, relevant context, suggested actions, or workflow automation.

An end-to-end conversational analytics system typically consists of four primary categories of AI agents:

• Data agents
• Planning agents
• Insight-synthesis agents
• Action agents

The following sections will describe these agents in detail.

Data agents

Function:  To retrieve relevant data from structured and unstructured sources based on user queries.

Data agents constitute the foundation of any conversational analytics system. Their central task is to retrieve pertinent data (structured, semi-structured, or unstructured) corresponding to a user's natural language query. Unlike traditional BI workflows where users must know data locations, schema, or SQL syntax, data agents abstract these complexities by intelligently routing queries to the appropriate data sources.

These agents are the first responders in the analytics pipeline. They interpret the intent of the query, establish which systems have the correct data, and retrieve the necessary information into the analysis stream.

Different data agents execute their respective activities based on the source type. Four common data agent types are:

• Database access agents: These agents interface with SQL and NoSQL databases to obtain their structured data. Take, for example, a user request to “Show revenue by product category for the last 6 months." The database access agent parses the query, maps it to pertinent tables and columns, and synthesizes the SQL query behind the scenes. The database access agent can also use metadata or semantic layers to deal with vague terms such as “revenue” or “product category.”
• Text agents: Text Agents handle unstructured data sources like PDF reports, support tickets, emails, and meeting transcripts. When prompted with a question like “What issues are customers reporting most frequently this month?”, the agent searches across multiple textual sources, performs semantic retrieval, and extracts the most relevant content using techniques such as embeddings, Retrieval-Augmented Generation (RAG), or named entity recognition.
• App-based agents: These agents pull data from enterprise applications such as CRMs (e.g., Salesforce), ERPs (e.g., SAP), or customer support systems (e.g., Zendesk). For instance, if a user asks, "What is the pipeline value in California today?", the app-based agent reads the CRM, performs the required filtering on the respective opportunities, and gives a concise answer.
• Public data access agents: In some cases, analysis requires blending internal enterprise data with external, publicly available sources. Public data access Agents fetch structured or semi-structured data from open datasets such as economic indicators, weather APIs, census data, or social media trends. For example, a retail analyst might ask, "How did local weather affect foot traffic over the weekend?" This agent would pull historical weather data to compare with internal sales or store visit data.

Planning agents

Function: To develop structured steps or workflows to achieve a user-defined analytical goal.

As data agents are concerned with locating the pertinent data, planning agents systematically determine how the data will be used. They serve as the 'Conductors' in a conversational analytics system, processing a user's free-text input into an analytical plan with several embedded steps. A user can state, for example, “Analyze Q1 sales and provide suggestions for Q2.”

The planning agent interprets this as a multi-part request and breaks it into a logical sequence of steps:

1. Get Q1 sales data across regions and product categories.
2. Identify underperforming segments and growth trends.
3. Compare current performance against historical benchmarks.
4. Define the scope for further analysis to surface Q2 strategies.

Insight-synthesis agents

Function: To combine data from multiple sources and generate meaningful insights.

Insight-synthesis agents are responsible for the most critical step in the analytics process: making sense of the data. While data agents retrieve information and Planning agents define the sequence of steps, insight-synthesis agents bring it all together. They analyze, correlate, and summarize data from various sources to surface insights that would otherwise require manual analysis or domain expertise.

For instance, a product manager may ask, “What are customers saying about our latest release, and how has that affected churn?” In response, the Insight-synthesis agent might:

• Aggregate customer feedback from surveys, support tickets, and app store reviews.
• Run sentiment analysis using an LLM or domain-specific model.
• Perform a correlation analysis between sentiment trends and churn rates.
• Generate a natural-language summary explaining key patterns, such as: “Customers experiencing frequent crashes post-update are 2.5x more likely to churn within 30 days.”

To enable this, Insight-synthesis agents often use a combination of generative AI and traditional analytics workflows, including LLMs for summarization and Python-based statistical analysis for time-series forecasting, regression modeling, and anomaly detection. For example, the agent may run a Python-based forecasting model on monthly revenue data while incorporating unstructured commentary from sales teams to contextualize dips or spikes.

Action agents

Function: To automate execution based on insights, bridging the gap between analytics and action.

Action agents represent the final stage in the conversational analytics pipeline where insights drive execution. These agents close the loop by automating tasks or triggering workflows.

Legacy analytics usually finishes the process at a report or dashboard. It's then up to a human being to take the next action based on the insights. Action agents remove the human bottleneck from this because they automate the decisions into action. There are some Action agents who can be described as follows:

• App-writeback agents: These agents automatically update external business tools when they detect actionable insights. For instance, if a particular group of users is flagged as at risk of canceling, the agent can instantly create follow-up tasks in the CRM or tag those users for a retention campaign. This closes the gap between finding a problem and taking action to address it.
• Communication agents: These agents deal with proactive messaging and reporting. They can produce and send contextual summaries, notify stakeholders, or even engage users through chat interfaces.
• Workflow automation agents: These agents orchestrate complex multi-step processes by interfacing with application programming interfaces (APIs) or automation platforms. Based on high-level goals, they might initiate a campaign in a marketing automation tool, reallocate cloud resources, or escalate operational issues. When integrated with Insight-synthesis agents, they act on the “so what?” by carrying out the “now what?”

Use cases for conversational analytics

The table below describes four popular conversational analytics use cases for AI agents.

Four best practices for AI agents in conversational analytics

The following best practices offer guidance for AI agent-based systems in conversational analytics. Organizations that implement these best practices can derive the benefits of conversational analytics while mitigating business risks.

Make data governance a priority

Organizations should ensure agents respect role-based access, resource-level permissions, and compliance policies. For example, a planning agent should not access HR data when executing a marketing-related query. Data access controls must be enforced across all agent interactions, especially as GenAI may process sensitive information.

Enable multi-agent collaboration

AI agents that can pass outputs and context to one another are more effective in conversational analytics use cases. For example, a data agent’s output should become input for an insight-synthesis agent. Define a common schema that agents adhere to when communicating across steps in the pipeline.

Require AI agent explainability and transparency across the board

Every AI agent should be traceable, especially those generating insights or triggering actions. This will help organizations to understand where the data came from, how it was handled, and what led to a particular suggestion or action. Having this kind of transparency helps build trust and is key when it comes to AI audits.

Enforce guardrails to reduce business risk

Not every recommendation should lead to immediate execution. Define thresholds, approval workflows, and safety margins that permit autonomous progression for an Action agent. The key to effective AI agent guardrails is balancing productivity with business risk.

For example, a human-in-the-loop could be required to update a financial system. On the other hand, launching an email campaign under a specified budget could be fully automated.

Adopt the Model Context Protocol (MCP) for context sharing

The Model Context Protocol (MCP) provides a standardized way for AI agents to securely exchange context with external systems, such as CRMs, ERP tools, and cloud APIs. By adopting MCP, organizations can ensure their AI agents operate with shared understanding, enforce context boundaries, and maintain data integrity across distributed components. MCP is particularly useful in orchestrated workflows involving multiple systems and agents, improving scalability, auditability, and modular design.

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Last thoughts

With conversational analytics, there is no doubt that businesses have seen an improvement in self-service BI. Natural language interfaces combined with AI agents enable highly contextual and action-driven insights to be rendered with speed and ease. This provides an unnaturally swift context for decision-making. The shift transforms the entire lifecycle of analytics, from querying to action planning to insight synthesis, eliminating the drawn-out waits and reliance on tech teams.

As generative AI continues to evolve, so will the improvement of conversational analytics platforms. Organizations that invest early in adopting collaborative, secure, and explainable multi-agent systems will be well positioned to unlock competitive advantages by answering “what’s happening” and knowing exactly what to do next.