Auto-Configure Instructions
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The Auto-Configure Instructions feature enables users to define how Osmos AI Data Agents (such as the AI Data Engineer) should transform and process source data, without requiring any code. Whether you provide instructions manually or let the AI derive them from documentation in a folder, this feature puts you in control of the configuration logic. At the same time, the AI does the heavy lifting.
Auto-Configure is an intelligent setup assistant that helps you:
Create a structured instruction set for your AI agent
Define destination schema and transformation logic
Provide either typed instructions or a folder of documentation
Preview and edit the AI-generated configuration before execution
It transforms your documentation, business rules, or direct input into clean, human-readable instructions the AI can follow for repeatable, controlled data processing.
Select Add Instructions
Select Manually provide Instructions
Now you have the option to upload a folder and/or provide instructions manually.
Directly input your configuration logic using a structured form:
Destination Tables: Specify where your output should go. This often pre-populates from your Fabric workspace.
Source Files: Identify what data is being transformed.
Ingestion Instructions: Describe transformations, validation rules, mappings, and business logic.
✅ Best for cases where you know exactly what the AI needs to do or when dealing with new, one-off logic.
Point the AI to a folder containing relevant documentation. The AI will:
Read all provided files (up to 10)
Extract transformation logic, schemas, and business intent
Generate editable instructions from:
Business requirements docs
Data models and schema designs
Code snippets or prior scripts (SQL, Python, etc.)
Sample source/output files
✅ Best for existing projects, historical context, or when repurposing prior data transformation logic.
The Osmos AI Data Engineer uses generative AI to analyze your inputs and convert them into a structured instruction set, including:
Target schema details
Source-to-destination mapping logic
Transformation functions and validations
Edge cases and data quality checks
These instructions act as guardrails for the AI, helping it:
Stay aligned with business rules
Ensure data integrity
Avoid brittle or incorrect transformations
Let’s say your destination is a employee_pets table, and you want the AI to extract employee and pet information from messy spreadsheets. You could either:
Manually configure: “Destination table is employee_pets. Use all files in the folder. Map emp_type to one of [Full-time, Part-time, Contractor]. Standardize phone numbers. Extract date from header.”
Use a folder: Upload a folder containing:
A document describing the target schema
A sample table of cleaned data
A script with useful regex patterns
Notes about mapping rules
The AI will parse that information and present it back to you as an editable instruction template. You can then adjust as needed.
After reviewing the generated instructions, you can:
Edit them inline
Add edge-case handling
Strengthen constraints (e.g., "fail if source columns change")
If the result isn't right, update your instructions and regenerate
Use real-time feedback to refine and guide the AI’s behavior
When using a folder to generate instructions:
Limit of 10 files per instruction set
All files must be relevant to the current data use case
Accepted formats include DOCX, PDF, TXT, CSV, XLSX, SQL, and code files
