How Agentic AI Builds Software Reports

These days, almost no one is still building reports by hand or simply using the reports that their software, CRM, or other platforms have. Like everything else, people are using an AI agent to build custom reports, so custom that they can edit them on the fly, have an agent make several iterations, and build the same report from several different angles.

For example, DuckBuddy (link) is a vibrant online social platform that connects remote workers with like-minded peers for collaborative work sessions and community building. There are thousands of user profiles, each rich with details about location, industry, and personality traits. But the current analytics dashboard doesn’t offer aggregated views or easy comparisons across your entire user base and even the data analytics platform that it used doesn't quite get you what you need.

Like all companies trying to get ahead or stay ahead, agentic AI is the best solution to create a report that really nails down into what a company needs to see. Requests like: “Analyze user profiles and create charts showing where our users are located, what industries they work in, and their personality types so we can see which customers are product is truly serving” are the questions that most traditional data analytics can't get you. Agent AI gives you much deeper insight.

To me, learning how an agent builds these reports is fascinating and like with anything, learning how a tool works, helps you use that tool better!

Example- DuckBuddy’s Architecture

DuckBuddy’s frontend is built with JavaScript and React, creating a dynamic, component-based interface. Profiles load asynchronously, with data fetched through a Python service layer and stored in a PostgreSQL (among several non SQL's)backend. This modern stack delivers a smooth user experience but introduces complexity for automated data extraction:

• React components render profile sections using unique class names, data attributes, or component-specific IDs that change with updates or builds.
• User information (location, industry, personality) appears in cards, tabs, or expandable sections rather than simple static tables.
• Navigation to individual profiles, pagination of user lists, and permission checks add layers the agent must understand.

An agent cannot simply “know” DuckBuddy’s layout. It must first learn where the “Location” field, “Industry Tags,” and “Personality Profile” elements live before it can reliably scrape the data.

How an Agent Delivers the Report

A well-guided agent typically follows this workflow:

1. Learn the Interface Layout: The agent explores the DuckBuddy profile page structure—identifying React-rendered elements for location (e.g., a city/country badge), industry (multi-select tags or dropdown values), and personality (quiz results or badge system).
2. Navigate and Extract Data: Starting from a user directory or search page, it systematically visits profiles, locates the relevant sections, and pulls the exact values.
3. Store and Structure the Data: Information is saved into a clean file (CSV or JSON) with columns like UserID, Location, Industry, Personality_Type, etc.
4. Analyze and Visualize: The agent filters, aggregates, and generates insightful charts:
   • Geographic Distribution: A world map or bar chart showing top countries/cities.
   • Industry Breakdown: Pie or donut chart displaying percentages (Tech, Marketing, Design, Consulting, etc.).
   • Personality Insights: Grouped bar charts or heatmaps comparing personality types across industries or locations.
   • Cross-comparisons: Stacked charts revealing patterns, such as “Remote developers in Europe tend to share these personality traits.”

The final output is a ready-to-share report—PDF, dashboard images, or even an interactive HTML file—without touching your PostgreSQL database directly or requiring backend changes.

Game-Changing Reporting

Your platform already collects rich profile data to power matching and community features. However, turning that scattered information into strategic insights (e.g., “Our fastest-growing segment is creative professionals in Southeast Asia”) typically demands significant development effort. An agent bypasses this by working with the existing frontend, giving you fast, on-demand analytics that inform marketing, product decisions, and community building.

Use Precise Prompts for Best Results

To maximize success and minimize token usage and corrections, provide the agent with specific context:

• Describe the Layout: “On DuckBuddy profile pages, the user’s location appears in a React component with class ‘profile-location-badge’. Industry tags are in a section with data-testid=‘industry-tags’. Personality results display as colored badges under the ‘Vibe’ heading.”
• Use Exact Terminology: Reference labels exactly as they appear: “Current City”, “Primary Industry”, “Personality Archetype”.
• Share HTML Snippets: Right-click on a profile element → Inspect → copy the relevant <div>, <span>, or React data attributes and paste them into your prompt. For example: <div class="profile-location-badge" data-location="Lisbon, Portugal">…</div>
• Include Navigation Details: Mention how to reach profiles (“From the Community Directory, click user cards which have hrefs containing /profile/”).

By front-loading this information, the agent quickly maps the React component tree instead of trial-and-error exploration. This is especially helpful with dynamic JavaScript-rendered content, where timing and element visibility matter.

The Human-Agent Partnership

Even sophisticated agents benefit from human guidance. You understand your platform’s nuances best—exact field names, permission flows, and edge cases (private profiles, incomplete data). By investing a few minutes in detailed prompts and HTML examples, you help the agent deliver accurate, efficient results. In return, it handles the repetitive scraping, data cleaning, and visualization work that would otherwise consume hours or require developer time.

Bottom Line

For platforms like DuckBuddy, agentic AI offers a practical bridge between existing user data and immediate business intelligence. Rather than using a "one-shot magic command", savvy AI operators provide layout context, exact terms, and inspected HTML elements. The result is faster learning, lower costs (tokens), and richer insights—geographic heatmaps, industry distributions, personality trend graphs—all generated from profiles that users already maintain.

If you master this prompting discipline, agentic AI will become one of your most valuable management and growth tools you use at work. The data that you want to see is already there and precise human direction can guide an agent to create exactly what you need!

*DuckBuddy is an online social networking platform concept that I created in order to have a concrete example of a company and company date in order to learn from. *