AI Agent
The ONE AI Agent is designed to radically transform data management by providing a general-purpose tool for automating many of the most tedious tasks. The current version of the ONE AI Agent focuses primarily on data quality.
For detailed use case walkthroughs and example prompts organized by workflow, see AI Agent Handbook.
Before you start
Take a moment to get familiar with the recommended practices for using Generative AI capabilities of ONE, described in Gen AI Best Practices. This will help you use Generative AI more effectively.
Using the AI Agent requires specific identity provider roles. See Reserved identity provider roles.
What is an AI agent
An AI agent is an intelligent system that uses Large Language Models (LLM) to perform complex tasks on the behalf of users.
In contrast to AI assistants or chatbots that primarily answer questions, AI agents have the ability to dynamically plan, refine, and execute plans that utilize API-based tools. These plans are not rigid workflows but are rather dynamic steps that are planned and validated as the agent works.
ONE AI Agent
| The AI model is not trained using your data or metadata. |
In ONE, the AI Agent is a general-purpose tool that can handle both atomic tasks and questions like a typical AI assistant as well as manage and plan for complex, multi-step tasks. This means the Agent is capable of completing a broad array of tasks across Ataccama ONE.
The AI Agent plays a key role in optimizing data management through:
- Intelligent task planning
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Creating and executing plans for complex tasks across the platform, not limited to just data quality.
- Specialized tool utilization
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Leveraging various tools for searching, accessing metadata and data, modifying content, and performing utility functions.
- Automated data management
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Reducing manual effort by handling tasks like data quality checks, rule generation, and catalog management.
- Data exploration and analysis
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Finding specific information in your data and generating insights based on patterns and trends.
- Documentation assistance
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Answering questions about platform functionality directly from documentation.
By integrating automation, intelligence, and compliance capabilities, the AI Agent in Ataccama ONE enhances data governance, decision-making, and operational efficiency, which in turn helps you manage data with greater confidence and precision.
AI Agent capabilities
The AI Agent uses the following tools to form plans that can execute complex work:
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Complex requests can take longer than simple queries. For example, bulk creation of data quality rules could take three minutes and more to fully run. Be clear about your question and expectations. |
Search and discovery
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Catalog search: Searches catalog items by keywords. Returns references, attribute or row counts, and match reasons with pagination.
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Business terms search: Searches the business glossary by keywords. Returns term references with pagination.
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Rule search: Finds data quality (DQ) rules using combined keyword and semantic matching on name, description, and keywords.
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Reference data table search: Finds reference data tables by name substring. Returns table references with a pagination cursor.
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Search transformation plans: Finds transformation plans by name, type, or last run state. Returns references with pagination.
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Search transformation rules: Finds transformation rules by name. Returns references with pagination.
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User groups list: Lists user groups with optional name filter and pagination. Useful for finding candidate steward groups.
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Documentation search: Answers natural-language questions against Ataccama ONE documentation. Returns a response and source references.
Catalog inspection
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Catalog detail: Retrieves comprehensive catalog item details including terms, relationships, DQ monitors, assigned rules, and ancestor hierarchy.
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Catalog item attributes: Lists a catalog item’s attributes (columns) along with any assigned terms and rules. Paginated.
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Data Trust Index: Fetches the Data Trust Index score (0-100) for a catalog item with a brief summary.
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DQ job results: Returns DQ job runs with pass or fail counts and overall score for a catalog item’s DQ monitor. Supports pagination.
Reference data table management
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Create table: Creates a new empty reference data table in draft.
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Create attribute: Adds a new column to a reference data table in draft. Requires applying schema changes afterward.
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Remove attribute: Deletes a column from a reference data table in draft (blocked if referenced). Requires applying changes afterward.
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Rename table: Renames a reference data table.
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Rename attribute: Renames a column in a reference data table draft. Requires applying changes afterward.
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Set table description: Sets or updates the description of a reference data table.
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Generate table description: Generates an AI description for a reference data table based on its schema and sample data.
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Change attribute data type: Changes a column’s data type in draft. Requires applying changes afterward.
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Connect attribute: Creates a foreign-key reference from one reference data table attribute to another table’s attribute (types must match). Requires applying changes afterward.
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Apply metadata changes: Publishes all pending draft schema changes for a reference data table.
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Fetch table attributes: Returns the schema (attributes, types) for a reference data table, including any reference attributes.
Reference data records
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Create or update records: Creates or updates records in a reference data table draft from provided objects. Supports record IDs for updates.
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Edit records: Executes a single-column SQL UPDATE against a reference data table draft. Returns the number of affected rows.
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Delete records: Deletes records from a reference data table draft using a detailed filter. Deletions can be undone by discarding the draft.
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Discard record changes: Discards draft record changes and restores last-published values matching a filter.
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Sample data: Returns up to 50 sample rows from a reference data table with optional filtering and sorting.
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Auto-import: Imports reference data from a catalog item into a reference data table via an auto-plan. Runs asynchronously and requires approval.
Data quality
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Create DQ rule: Creates a new DQ evaluation rule from a name, business description, inputs, and optional dimension. Aggregation and parameterized rules are not supported.
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Assign rule to catalog item: Assigns a DQ rule to catalog item attributes, validating intent, input mapping, and data types.
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Assign rule to reference data table: Assigns a DQ rule to one or more reference data table attributes. Supports multi-input or parameterized rules and WARN or BLOCK mode.
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Profile anomalies: Retrieves profiling anomalies and basic profiling information for a catalog item or run.
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Profile statistics: Fetches attribute-level profiling stats including distribution, masks, and patterns.
Governance and metadata enrichment
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Assign stewardship: Assigns stewardship of a catalog item or rule to a user group.
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Assign term to attribute: Links a glossary term to a catalog item attribute. Creates a draft term-attribute link.
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Assign term to catalog item: Links a glossary term to a catalog item. Creates a draft term-catalog item link.
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Edit description: Edits the description of a catalog item, term, or rule.
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Generate description: Generates or improves an AI-written description for catalog items, attributes, rules, or terms.
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Rename entity: Renames a catalog item, term, or rule.
Transformation plans and rules
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Manage plan or rule lifecycle: Publishes, discards, deletes, or confirms deletion for transformation plans and rules (not DQ rules).
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Run transformation plan: Starts an immediate one-time run of a published standalone transformation plan.
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Configure plan schedule: Creates or modifies a recurring cron schedule for a standalone transformation plan.
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Control plan schedule: Deletes, pauses, or resumes a plan’s existing schedule.
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Generate transformation rule: Generates a new transformation rule from a natural-language prompt using AI. Returns a draft.
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Describe plan or rule: Generates an AI summary of a transformation plan or rule.
SQL and catalog creation
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Create SQL catalog item: Creates a new SQL catalog item from a base catalog item and a SQL query, with name and description.
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SQL query: Executes a SQL query on a catalog item to retrieve data (SQL-compatible items only).
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Data sample: Retrieves a small random data sample for each attribute to understand structure.
Limitations of the AI Agent
The AI Agent can intelligently chain together the tools and capabilities listed in AI Agent capabilities. When these tools are combined in agentic workflows, you can address different use cases in the platform.
Since there is no technical limit to the requests you can send, there is no strict limit to what you can achieve.
While we can’t provide an exhaustive list of possible use cases, we actively test and support those listed in the AI Agent Handbook. Results for unlisted use cases can’t be guaranteed.
The AI Agent currently cannot:
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Generate complete transformation plans end-to-end.
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Read all metadata from catalog items.
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Edit all fields within catalog items.
How to use AI Agent
Open the AI Agent chat window at any time from the left-side navigation menu.
Additionally, the AI Agent is available throughout the platform, for example in the Catalog or Glossary. When accessed from these locations, the AI Agent can provide context-aware assistance relevant to the assets.
| Look for the star icon in the upper-right corner to open the AI Agent from these locations. |
AI Agent interface
When you open the AI Agent, a chat window appears with a field where you describe your task.
The interface includes the following elements.
Toolbar
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New chat (plus icon): Open a new chat.
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Chat history (clock icon): Access previous chats.
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More options (three dots): Access Prompt library or Settings.
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Close (X icon): Close the AI Agent window.
Start a new chat
To start a new chat, select the new chat icon. Write a query in natural language and press Enter or select the arrow.
You can also choose from the prompt library or select one of the suggested prompts.
In the following example, we ask the AI Agent to find attributes of a specific catalog item, and let it suggest rules for it.
Find attributes of @DQ_CI_DIMENSIONS_FACTS with no rules applied, and suggest appropriate rules for them.
| Use @ to select entities and data assets when writing queries. |
I have identified that all attributes of the catalog item
DQ_CI_DIMENSIONS_FACTS do not have any rules assigned to them. Based on their data types, I have suggested appropriate rules for each attribute:
STRING Attributes: For attributes like REPORT_ID, PROCESSING_ID, CONFIGURATION_ID, CONFIGURATION_NAME, CATALOG_ITEM_ID, CATALOG_ITEM_NAME, DIMENSION_ID, and DIMENSION_NAME, I suggest a rule to check that the value is not empty.
DATETIME Attributes: For attributes like PROCESSING_STARTED_AT and PROCESSING_FINISHED_AT, I suggest a rule to check that the value is a valid date format.
LONG Attributes: For attributes like FAILED_COUNT, PASSED_COUNT, and TOTAL_COUNT, I suggest a rule to check that the value is non-negative.
Interact with AI Agent responses
After you submit a query, the AI Agent processes your request and displays the response.
You can interact with the results in one of several ways:
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Steps: Select Show steps to see the steps the AI Agent took to complete your request. Each step shows the action taken, such as retrieving data or creating items. Select Hide steps to close this view.
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Review changes: If the AI Agent made changes during the conversation, you can see a summary in Review changes.
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Continue the conversation: Use the query field to ask follow-up questions or refine your request. Select Add context to reference additional entities using the
@symbol.
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