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Data quality agent & DBMS Scheduler agent
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‎autonomous-ai-agents/data_quality_agent/README.md‎

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# Select AI - Data Quality Check Agent for Oracle Autonomous Database
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## Release Metadata
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- Release Version: `1.1`
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- Release Date: `19-May-2026`
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## Overview
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The **Data Quality Check Agent** provides schema-aware data quality assessment for Oracle Autonomous Database tables using Select AI Agent tools.
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It supports:
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- Table profiling
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- Null/duplicate/outlier detection
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- Quality score computation with history tracking
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- Drift detection based on recent vs baseline score windows
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- Issue listing with severity and remediation guidance
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- Safe remediation preview and controlled apply mode
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- OML Services monitoring setup and run trigger hooks
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For definitions of **Tool**, **Task**, **Agent**, and **Agent Team**, see the top-level guide: [README](../README.md#simple-agent-execution-flow).
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---
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## Repository Contents
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```text
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.
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├── database_quality_check_tools.sql
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│ ├── Installer bootstrap and grants
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│ ├── DATABASE_QUALITY package (core DQ logic)
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│ ├── SELECT_AI_DATA_QUALITY_AGENT package (tool wrappers)
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│ └── Tool registration
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│
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├── database_quality_check_agent.sql
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│ ├── Task definition (DATA_QUALITY_TASKS)
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│ ├── Agent creation (DATA_QUALITY_ADVISOR)
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│ ├── Team creation (DATA_QUALITY_TEAM)
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│ └── Default target schema behavior (DQ_TARGET_SCHEMA)
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│
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└── README.md
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```
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---
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## Architecture Overview
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```text
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User Request
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↓
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DATA_QUALITY_TASKS
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↓
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DATA_QUALITY_ADVISOR Reasoning
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├── PROFILE_TABLE_TOOL
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├── DETECT_NULLS_TOOL
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├── DETECT_DUPLICATES_TOOL
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├── DETECT_OUTLIERS_TOOL
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├── DETECT_DRIFT_TOOL
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├── GENERATE_QUALITY_RULES_TOOL
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├── EVALUATE_QUALITY_SCORE_TOOL
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├── LIST_QUALITY_ISSUES_TOOL
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├── SUGGEST_REMEDIATION_TOOL
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├── APPLY_REMEDIATION_TOOL
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├── SETUP_OML_DATA_MONITORING_TOOL
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└── RUN_OML_DATA_MONITORING_TOOL
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↓
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Issue Summary + Severity + Quality Score + Next Action
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```
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---
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## Prerequisites
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- Oracle Autonomous AI Database (26ai recommended)
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- Select AI and `DBMS_CLOUD_AI_AGENT` enabled
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- `ADMIN` (or equivalent privileged user) for installation
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- A valid AI profile (`DBMS_CLOUD_AI.CREATE_PROFILE`)
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- Object privileges from install schema to target data schema tables (for cross-schema checks)
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For OML monitoring tools:
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- `SELECTAI_AGENT_CONFIG` entries for `AGENT='DATA_QUALITY'`:
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- `OML_MONITORING_ENDPOINT`
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- `OML_MONITORING_CREDENTIAL`
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For controlled remediation apply:
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- `SELECTAI_AGENT_CONFIG` entry for `AGENT='DATA_QUALITY'`:
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- `REMEDIATION_APPROVAL_CODE`
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---
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## Installation
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Run as `ADMIN` (or privileged user) from this folder:
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```sql
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sqlplus admin@<adb_connect_string> @database_quality_check_tools.sql
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sqlplus admin@<adb_connect_string> @database_quality_check_agent.sql
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```
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Prompts in tools script:
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- `SCHEMA_NAME` (schema where package/tools are installed)
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Prompts in agent script:
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- `SCHEMA_NAME` (same install schema)
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- `AI_PROFILE_NAME`
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- `DQ_TARGET_SCHEMA` (default schema for DQ checks; if blank uses `SCHEMA_NAME`)
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Important:
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- Re-run `database_quality_check_agent.sql` whenever task instructions are changed.
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---
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## Internal Tables
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Created in install schema (if missing):
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- `DQ_RUN_HISTORY$`:
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- score history per table/run
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- stores score component metrics and worst severity
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- `DQ_FINDINGS$`:
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- issue registry with severity, recommendation, and optional fix SQL
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- `DQ_OML_MONITORS$`:
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- registered OML monitor metadata and last run response
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---
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## Tool-to-Function Mapping
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| Tool | Function | Purpose |
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|---|---|---|
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| `PROFILE_TABLE_TOOL` | `select_ai_data_quality_agent.profile_table` | Baseline profile |
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| `DETECT_NULLS_TOOL` | `select_ai_data_quality_agent.detect_nulls` | Null issue detection |
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| `DETECT_DUPLICATES_TOOL` | `select_ai_data_quality_agent.detect_duplicates` | Duplicate detection |
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| `DETECT_OUTLIERS_TOOL` | `select_ai_data_quality_agent.detect_outliers` | Outlier detection |
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| `DETECT_DRIFT_TOOL` | `select_ai_data_quality_agent.detect_drift` | Drift analysis |
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| `GENERATE_QUALITY_RULES_TOOL` | `select_ai_data_quality_agent.generate_quality_rules` | Rule suggestions |
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| `EVALUATE_QUALITY_SCORE_TOOL` | `select_ai_data_quality_agent.evaluate_quality_score` | Score + persistence |
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| `LIST_QUALITY_ISSUES_TOOL` | `select_ai_data_quality_agent.list_quality_issues` | Issue review |
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| `SUGGEST_REMEDIATION_TOOL` | `select_ai_data_quality_agent.suggest_remediation` | SQL guidance |
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| `APPLY_REMEDIATION_TOOL` | `select_ai_data_quality_agent.apply_remediation` | Preview/apply fix SQL |
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| `SETUP_OML_DATA_MONITORING_TOOL` | `select_ai_data_quality_agent.setup_oml_data_monitoring` | Register OML monitor |
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| `RUN_OML_DATA_MONITORING_TOOL` | `select_ai_data_quality_agent.run_oml_data_monitoring` | Trigger OML monitor run |
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---
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## Operational Behavior
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- If `owner_name` is omitted, agent defaults to `DQ_TARGET_SCHEMA`.
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- For schema-wide requests (for example, “all tables”), task instruction is configured to auto-discover tables and not ask user to list table names.
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- `APPLY_REMEDIATION_TOOL`:
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- default mode is `PREVIEW`
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- `APPLY` requires matching `approval_code`
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- SQL safety checks block unsafe statements
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---
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## Example Prompts
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- `Check null issues in SALES and show columns with null_count, null_rate_pct, and severity.`
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- `Detect duplicates in SALES using all columns and show duplicate_row_count, duplicate_rate_pct, and severity.`
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- `Find numeric outliers in SALES using z-score threshold 3 and rank by severity.`
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- `Evaluate quality score for SALES and explain null, duplicate, outlier, and drift components.`
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- `Evaluate quality score for every table in the default target schema and return table-wise summary.`
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- `List open HIGH severity quality issues for SALES with recommendation and generated_fix_sql.`
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- `Preview remediation for issue_id 1 on SALES.`
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- `Apply remediation for issue_id 1 on SALES with execute_mode APPLY and approval_code <code>.`
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OML examples:
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- `Set up OML data monitoring for SH.SALES with monitor name SH_SALES_DQ_MON, baseline query "<baseline_sql>", and new-data query "<new_sql>".`
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- `Run OML data monitoring for monitor SH_SALES_DQ_MON and return the job response.`
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---
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## Troubleshooting
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- `ORA-00942` during package compilation:
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- Re-run `database_quality_check_tools.sql`; it pre-creates `DQ_*` tables.
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- Agent asks for table list during “all tables” request:
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- Re-run `database_quality_check_agent.sql` to recreate task with latest instructions.
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- OML monitoring tool errors with missing config:
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- Insert required keys in `SELECTAI_AGENT_CONFIG` for `AGENT='DATA_QUALITY'`.
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- Apply remediation blocked:
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- Ensure `REMEDIATION_APPROVAL_CODE` is configured and supplied as `approval_code`.

‎autonomous-ai-agents/data_quality_agent/database_quality_check_agent.sql‎

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rem ============================================================================
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rem LICENSE
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rem Copyright (c) 2026 Oracle and/or its affiliates.
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rem Licensed under the Universal Permissive License (UPL), Version 1.0
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rem https://oss.oracle.com/licenses/upl/
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rem
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rem NAME
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rem database_quality_check_agent.sql
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rem
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rem DESCRIPTION
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rem Installer and configuration script for Data Quality Check AI Agent Team.
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rem
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rem RELEASE VERSION
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rem 1.0
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rem
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rem RELEASE DATE
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rem 18-May-2026
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rem ============================================================================
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SET SERVEROUTPUT ON
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SET VERIFY OFF
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PROMPT ======================================================
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PROMPT Data Quality Check AI Agent Installer
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PROMPT ======================================================
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VAR v_schema VARCHAR2(128)
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EXEC :v_schema := '&SCHEMA_NAME';
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VAR v_ai_profile_name VARCHAR2(128)
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EXEC :v_ai_profile_name := '&AI_PROFILE_NAME';
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PROMPT
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PROMPT DQ_TARGET_SCHEMA:
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PROMPT Schema to inspect by default for data quality checks.
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PROMPT If blank, SCHEMA_NAME is used as default.
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PROMPT
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VAR v_dq_target_schema VARCHAR2(128)
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EXEC :v_dq_target_schema := '&DQ_TARGET_SCHEMA';
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DECLARE
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l_sql VARCHAR2(500);
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l_schema VARCHAR2(128);
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l_session_user VARCHAR2(128);
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BEGIN
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l_schema := DBMS_ASSERT.SIMPLE_SQL_NAME(:v_schema);
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l_session_user := SYS_CONTEXT('USERENV', 'SESSION_USER');
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IF UPPER(l_schema) <> UPPER(l_session_user) THEN
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l_sql := 'GRANT EXECUTE ON DBMS_CLOUD_AI_AGENT TO ' || l_schema;
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EXECUTE IMMEDIATE l_sql;
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l_sql := 'GRANT EXECUTE ON DBMS_CLOUD_AI TO ' || l_schema;
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EXECUTE IMMEDIATE l_sql;
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l_sql := 'GRANT EXECUTE ON DBMS_CLOUD TO ' || l_schema;
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EXECUTE IMMEDIATE l_sql;
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ELSE
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DBMS_OUTPUT.PUT_LINE('Skipping grants for schema ' || l_schema ||
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' (same as session user).');
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END IF;
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DBMS_OUTPUT.PUT_LINE('Grants completed.');
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END;
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/
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BEGIN
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EXECUTE IMMEDIATE 'ALTER SESSION SET CURRENT_SCHEMA = ' || :v_schema;
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END;
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/
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CREATE OR REPLACE PROCEDURE install_data_quality_check_agent(
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p_install_schema IN VARCHAR2,
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p_profile_name IN VARCHAR2,
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p_dq_target_schema IN VARCHAR2
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)
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AUTHID DEFINER
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AS
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l_target_schema VARCHAR2(128);
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BEGIN
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l_target_schema := UPPER(TRIM(NVL(p_dq_target_schema, p_install_schema)));
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DBMS_OUTPUT.PUT_LINE('--------------------------------------------');
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DBMS_OUTPUT.PUT_LINE('Starting Data Quality Check AI installation');
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DBMS_OUTPUT.PUT_LINE('--------------------------------------------');
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BEGIN
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DBMS_CLOUD_AI_AGENT.DROP_TASK('DATA_QUALITY_TASKS');
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EXCEPTION
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WHEN OTHERS THEN
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NULL;
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END;
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DBMS_CLOUD_AI_AGENT.CREATE_TASK(
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task_name => 'DATA_QUALITY_TASKS',
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description => 'Task for data quality profiling, scoring, and remediation planning',
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attributes => '{
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"instruction": "You are a Data Quality specialist for Oracle Autonomous Database. '
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|| 'Default target schema for data quality checks is ' || l_target_schema || '. '
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|| 'If the user does not provide owner_name, use owner_name=' || l_target_schema || '. '
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|| 'If the user provides a different schema explicitly, use that schema. '
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|| 'Cross-schema analysis is allowed when object privileges are granted to the install schema. '
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|| 'When the user asks for all tables or schema-wide analysis, automatically discover table names from the target schema and run checks without asking the user to provide table lists. '
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|| 'If no owner_name is provided in schema-wide requests, use owner_name=' || l_target_schema || '. '
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|| 'Do not ask the user for table names when this can be derived from ALL_TABLES/USER_TABLES metadata. '
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|| 'Use PROFILE_TABLE_TOOL first to establish table baseline when user provides owner/table. '
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|| 'Use DETECT_NULLS_TOOL, DETECT_DUPLICATES_TOOL, and DETECT_OUTLIERS_TOOL to identify quality issues with severity. '
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|| 'Use GENERATE_QUALITY_RULES_TOOL to propose enforceable quality rules. '
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|| 'Use EVALUATE_QUALITY_SCORE_TOOL to compute/store overall quality score and history point. '
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|| 'Use DETECT_DRIFT_TOOL to identify recent score drift against baseline history. '
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|| 'Use SETUP_OML_DATA_MONITORING_TOOL for automated OML Services data monitoring setup when requested. '
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|| 'Use RUN_OML_DATA_MONITORING_TOOL to trigger OML monitoring jobs and report run response. '
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|| 'Use LIST_QUALITY_ISSUES_TOOL for issue review. '
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|| 'Use SUGGEST_REMEDIATION_TOOL to produce practical SQL-based fixes. '
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|| 'Only use APPLY_REMEDIATION_TOOL in PREVIEW mode unless the user explicitly asks to apply changes and provides approval_code. '
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|| 'Always return: issue summary, severity, quality score, and next remediation step. '
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|| 'User request: {query}",
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"tools": [
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"PROFILE_TABLE_TOOL",
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"DETECT_NULLS_TOOL",
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"DETECT_DUPLICATES_TOOL",
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"DETECT_OUTLIERS_TOOL",
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"DETECT_DRIFT_TOOL",
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"SETUP_OML_DATA_MONITORING_TOOL",
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"RUN_OML_DATA_MONITORING_TOOL",
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"GENERATE_QUALITY_RULES_TOOL",
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"EVALUATE_QUALITY_SCORE_TOOL",
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"LIST_QUALITY_ISSUES_TOOL",
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"SUGGEST_REMEDIATION_TOOL",
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"APPLY_REMEDIATION_TOOL"
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],
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"enable_human_tool": "true"
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}'
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);
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DBMS_OUTPUT.PUT_LINE('Created task DATA_QUALITY_TASKS');
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BEGIN
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DBMS_CLOUD_AI_AGENT.DROP_AGENT('DATA_QUALITY_ADVISOR');
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EXCEPTION
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WHEN OTHERS THEN
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NULL;
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END;
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DBMS_CLOUD_AI_AGENT.CREATE_AGENT(
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agent_name => 'DATA_QUALITY_ADVISOR',
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attributes =>
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'{' ||
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'"profile_name":"' || p_profile_name || '",' ||
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'"role":"You are a Data Quality Advisor. You profile data, detect anomalies and drift, compute quality scores, and recommend safe remediation steps for Oracle Autonomous Database tables."' ||
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'}',
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description => 'AI agent for Oracle Autonomous Database data quality monitoring and remediation guidance'
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);
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DBMS_OUTPUT.PUT_LINE('Created agent DATA_QUALITY_ADVISOR');
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BEGIN
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DBMS_CLOUD_AI_AGENT.DROP_TEAM('DATA_QUALITY_TEAM');
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EXCEPTION
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WHEN OTHERS THEN
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NULL;
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END;
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DBMS_CLOUD_AI_AGENT.CREATE_TEAM(
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team_name => 'DATA_QUALITY_TEAM',
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attributes => '{
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"agents":[{"name":"DATA_QUALITY_ADVISOR","task":"DATA_QUALITY_TASKS"}],
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"process":"sequential"
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}'
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);
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DBMS_OUTPUT.PUT_LINE('Created team DATA_QUALITY_TEAM');
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DBMS_OUTPUT.PUT_LINE('--------------------------------------------');
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DBMS_OUTPUT.PUT_LINE('Data Quality Check AI installation COMPLETE');
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DBMS_OUTPUT.PUT_LINE('--------------------------------------------');
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END install_data_quality_check_agent;
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/
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PROMPT Executing installer procedure ...
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BEGIN
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install_data_quality_check_agent(
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p_install_schema => :v_schema,
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p_profile_name => :v_ai_profile_name,
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p_dq_target_schema => :v_dq_target_schema
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);
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END;
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/
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ALTER SESSION SET CURRENT_SCHEMA = ADMIN;
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PROMPT ======================================================
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PROMPT Installation finished successfully
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PROMPT ======================================================

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