Model Output Insights AI Agent

Overview

The Model Output Insights Agent helps users investigate and analyze Cosmic Frog model outputs by turning analytical questions into structured, data-backed strategic reports. It breaks down complex questions into a step-by-step exploration plan, executes targeted queries, synthesizes findings, and produces a professional report - complete with visualizations and actionable recommendations.

This documentation describes how this specific agent works and can be configured, including walking through an example. Please see the “AI Agents: Architecture and Components” Help Center article if you are interested in understanding how the Optilogic AI Agents work at a detailed level.

Why It's Useful

Extracting meaningful insights from large databases typically requires exploring and analyzing many output tables which can take a lot of time and effort. The Model Output Insights Agent streamlines the process, helping users get to the insights quicker than ever before.

• Enhances productivity by automating complex research, analysis, and reporting tasks.
• Delivers high-quality, data-backed executive reports suitable for decision-makers.
• Adaptable to a wide range of analytical domains, from business strategy to technical investigations.

Key Capabilities

Structured Exploration

• Operates using a to-do list approach, breaking down analytical questions into discrete, manageable tasks.
• Progresses methodically by selecting and addressing one task at a time, ensuring thoroughness and clarity.

Deep Analytical Reasoning

• Utilizes the Analyzer skill (see table below) to query databases, analyze data, and synthesize findings.
• Provides evidence-based insights and actionable recommendations.

Executive Reporting

• When sufficient findings are gathered, it invokes the Report Builder skill (see table below) to generate comprehensive, professional-style executive reports.
• Reports include sections such as Executive Summary, Situation Assessment, Problem Statement, Analytical Framework, Methodology, Key Findings, Scenario Analysis, Data Summary, Insights & Recommendations, Validation, Strategic Implications, and Next Steps.

Iterative and Adaptive

• Addresses one to-do per turn, allowing for focused analysis and adaptability based on interim findings.
• Can conclude early if enough information is obtained, optimizing efficiency.

Main skills the Model Output Insights Agent uses:

Supporting capabilities:

How To Use It

The agent can be accessed through the Run Utility task in DataStar. The key inputs are:

1. Cosmic Frog Model Name - The model that the user wants to analyze.
2. Analysis Questions - This is where the user asks the agent what they would like to know from the model outputs. It can be as simple as "Give me cost and flow comparison between Scenario A and Scenario B", or more involved/a longer list of questions.

Optionally, users can configure the following Run Utility task inputs:

• Knowledge Folder - Provide additional context for the agent to understand business background, modeling strategy, or even a request for a certain report style. The accepted file formats are md, csv, and txt. To use these files, we recommend creating a folder in the Explorer application, uploading the files to it, and entering the Folder Path as the Knowledge Folder input of the Run Utility Task. This screenshot shows how to get the Folder Path: 1) right-click on the folder in the Explore, 2) hover over Copy in the context menu, and 3) click on Folder Path:

• Report File Name - This field is populated with "exploration_report" by default, but users can specify their own name for the report file.
• Output Directory - Specify the folder name where the report and charts will be saved (default = Model Output Insights Agent). The system assumes that this is a folder name under My Files, so you do not need a full folder path here. This is different from what is needed for Knowledge Folder. For example, if the full folder path is /projects/My Files/Model Output Insights Agent_Report/ABC, only "Model Output Insights Agent_Report/ABC" is needed in this box.

After the run, a report in markdown format (.md) and possible charts are created and can be found in the Explorer with the specified file name and folder. Once clicked, the file is opened in the Lightning Editor application for review.

Note that currently the charts are only included in the markdown file as a file name. Users can look for the charts in the Charts folder in the targeted report directory:

The Run Utility task also offers the ability for users to set Run Configuration options. This is optional.

• Tags: Enter the tags for this task for easy filtering in the Run Manager application.
• Timeout: The time allowed for the task to run until being stopped.
• Resource Size: Different resource sizes offer different memory (RAM in Gb) and number of CPU cores to handle various task complexities. This only impacts ability to load the work into memory. The recommendation is to start with the default setting, monitor memory usage in the Run Manager application > Job Usage, and scale up if needed. The key principle is that a bigger resource does not always result in faster agent response time.

Other Helpful Notes

• If the Report File Name already exists in the Report Directory, a numbered suffix will be added to avoid overwriting the existing file - e.g., exploration_report (1).
• Runtime for the agent varies based on the amount of data to analyze and the complexity of the question(s). Expect at least 10 minutes of runtime.
• Just like many other DataStar tasks, it is possible to run multiple tasks in parallel with the Model Output Insights Agent.
• Additional info on the run can be found in Run Manager > Job Log after the run finishes. This includes steps that the agent takes, tools it calls, as well as a work summary. The AI Response sections are typically the most useful as they explain the exploration plan, the work it has done, and the results after exploration. This is generally a response to the user, while all others are more about internal processes.

Example

This example uses the Global Supply Chain Strategy model from the Resource Library to get insights on Baseline vs. No Detroit DC scenario comparison where cost, flow shifts and service impacts are explored.

Cosmic Frog Model Name: Global Supply Chain Strategy

Analysis Question: Compare cost and flow from Baseline and No Detroit DC scenarios. I'm interested in knowing the cost bucket that drives total savings. I want to know where the flow from Detroit DC was redirected to. Lastly, compare weighted average service distance - i.e. do customers have shorter/longer/the same service distance when Detroit closes down. Who are the top 5 customers with highest service impact?

Knowledge: Info on target audience for the report, expected report length and tone:

Should you wish to read the entire report instructions file and/or use it as a starting point for your own usage with this Agent, you can download it here. After downloading, please rename the .txt extension to .md. You can then upload it to your Optilogic account using the Explorer application and then view it in the Lightning Editor application.

Outputs: The report as a markdown file and a chart in the Charts folder:

Other Helpful Resources

• Learn more about Cosmic Frog
• Learn more about DataStar
• An overview of Optilogic's AI Agents and Utilities
• The Data Cleansing AI Agent, also available from within DataStar