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Feb 20, 2026
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Jira Inspect Agent V1 #65

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Jira Inspect Agent V1
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JIRA_INSPECT_AGENT
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157 changes: 96 additions & 61 deletions autonomous-ai-agents/README.md
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# Select AI Agents on Oracle Autonomous AI Database
# Using Select AI Agent on Oracle Autonomous AI Database

## Overview

This repository provides a **generic, extensible framework for building Select AI Agents on Oracle Autonomous AI Database** using the **Select AI Agent framework**.
This repository provides a set of extensible AI agent templates built using Select AI Agent on Oracle Autonomous AI Database.

Select AI Agent enables natural language interactions with enterprise data by combining large language models (LLMs), database-resident tools, and orchestration logic directly inside your database. Agents can reason over user input, invoke tools, and return structured, explainable results while keeping data governance, security, and execution within the database.

Select AI Agents enable natural language interactions with enterprise data by combining large language models (LLMs), database-resident tools, and orchestration logic directly inside Oracle Database. Agents can reason over user input, invoke tools, and return structured, explainable results — all while keeping data governance, security, and execution within the database.
The agents in this repository are templates that you can create and customize for enterprise use cases. While some examples interact with Oracle services, the Select AI Agent framework is not limited to a specific domain and can support many types of agents and workflows.

The agents in this repository are **generic Select AI agents**. While some examples may interact with Oracle services, the framework itself is not limited to any specific domain or platform and can support many different types of agents and workflows.
For product details, see:
https://docs.oracle.com/en-us/iaas/autonomous-database-serverless/doc/select-ai-agent.html

---

## What is a Select AI Agent?

Select AI Agents are part of the Oracle Autonomous AI Database Select AI framework. A Select AI Agent:
Select AI Agent is part of Oracle Autonomous AI Database and extends core Select AI capabilities, including NL2SQL and RAG.

- Accepts natural language input from users
A Select AI Agent:

- Accepts natural language input from users or programs
- Uses an LLM to reason about the request
- Invokes database-resident tools (PL/SQL functions)
- Executes logic securely inside the database
- Returns structured responses
- Invokes built-in tools and custom tools (enabled using PL/SQL functions)
- Performs logic securely inside the database
- Returns agent responses

Key characteristics of the Select AI Agent framework include:

- Native integration with Oracle Autonomous AI Database
- Tool execution through PL/SQL
- Support for tasks, agents, and teams
- Simple framework to build, deploy, and manage AI agents
- Native database integration to reduce infrastructure and orchestration overhead
- Support for preferred AI models and providers, including private endpoints
- Autoscaling in Oracle Autonomous AI Database
- Support for tools, tasks, agents, and teams
- Centralized governance and monitoring
- Flexibility to build domain-specific or generic agents

For full details, refer to the official documentation:
https://docs.oracle.com/en-us/iaas/autonomous-database-serverless/doc/select-ai-agent.html
---

## Simple Agent Execution Flow

```text
User -> Agent -> Task -> Tool (PL/SQL) -> Database -> Response
```

These concepts are represented as database-managed objects in the Select AI Agent framework.

| Concept | Definition |
|------|------------|
| Agent | An actor with a defined role that performs tasks using the LLM specified in the AI profile. |
| Task | A set of instructions that guides the LLM to use one or more tools to complete a step in a workflow. |
| Tool | A capability invoked by a task to perform actions, such as querying databases, calling web services, or sending notifications. |
| Agent Team | A group of agents with assigned tasks, serving as the unit for deploying and managing agent-based solutions. |

---

## Design Principles

### 1. Two-Layer Architecture
### Two-Layer Architecture

Each agent implementation follows a two-layer model using separate SQL scripts.

| Layer | Script Pattern | Purpose |
|--------------|-----------------|---------|
| Tools Layer | `*_tools.sql` | Installs core PL/SQL logic and registers Select AI tools |
| Agent Layer | `*_agent.sql` | Creates a sample Task, Agent, and Team using those tools |
| Layer | Script Pattern | Purpose |
|------|----------------|---------|
| Tools Layer | `*_tools.sql` | Installs core PL/SQL logic and registers Select AI tools |
| Agent Layer | `*_agent.sql` | Creates sample Task, Agent, and Team objects using those tools |

This design provides:

- **Tools** that are reusable across multiple agents
- **Agents** as examples for customizable behavior

The clear separation between tools and agents allows infrastructure logic to remain stable while agent behavior can be easily adapted or extended.
- Reusable tools across multiple agents
- Agent templates that can be customized for new domains and workflows
- Clear separation between infrastructure logic and agent behavior

---

Expand All @@ -59,56 +77,64 @@ The clear separation between tools and agents allows infrastructure logic to rem
The repository is organized to align with the Select AI Agent framework:

- Tools scripts define and register reusable PL/SQL functions
- Agent scripts demonstrate how those tools are composed into tasks, agents, and teams
- Agent scripts compose tools into tasks, agents, and teams
- Additional agents can be created without modifying existing tools

---

## Common Prerequisites for All Agents

Before installing any agent in this repository, ensure the following baseline prerequisites are met:

- Oracle Autonomous AI Database is provisioned
- Select AI and `DBMS_CLOUD_AI_AGENT` are enabled
- You are using `ADMIN` or another user with required privileges to create packages, grants, and agent objects
- Required network access and credentials are available for any external integrations used by the agent
- A Select AI profile is created using `DBMS_CLOUD_AI.CREATE_PROFILE`

Each agent subfolder may include additional service-specific prerequisites.

---

## Creating a Select AI Profile

Before using Autonomous Database AI agents, you must create a Select AI profile using DBMS_CLOUD_AI.
Before using Select AI Agent objects, create a Select AI profile with `DBMS_CLOUD_AI.CREATE_PROFILE`.

Oracle provides several examples demonstrating how to create AI profiles for different providers and models. See the documentation below and follow one of the examples to create your profile:
A Select AI profile is a configuration object that defines the AI provider and models (LLM and transformer) used by Select AI. It also stores provider metadata, credential references, and behavior settings used at runtime.

Select AI profile management documentation:
Profile management documentation:
https://docs.oracle.com/en-us/iaas/autonomous-database-serverless/doc/select-ai-manage-profiles.html#GUID-3721296F-14A1-428A-B464-7FA25E9EC8F3

Start by reviewing the examples in this documentation and create a profile appropriate for your environment (OCI Generative AI, OpenAI, Azure OpenAI, etc.). The profile name you create will be used later when configuring AI agents.
Create a profile appropriate for your environment (OCI Generative AI, OpenAI, Azure OpenAI, and others). The profile name is provided later when creating agent objects from `*_agent.sql`.

---

## Agent Configuration (`SELECTAI_AGENT_CONFIG`)

### Overview

Select AI agents use a shared configuration table named `SELECTAI_AGENT_CONFIG` to store **agent-specific configuration parameters**.

The table is generic and can be used by any Select AI agent (for example, NL2SQL data retrieval agents or other domain-specific agents). Each agent persists only the configuration keys it requires, while default behavior applies when values are not provided.
`SELECTAI_AGENT_CONFIG` is a shared configuration table used by agent installers and runtime code to store agent-specific parameters.

---
Each agent stores only the keys it needs (for example, credential names, feature flags, compartment names, or integration endpoints). Defaults can still be applied by tool logic when optional values are not present.

### Column Description

| Column | Description |
|------|------------|
| `ID` | System-generated unique identifier |
| `KEY` | Configuration parameter name |
| `VALUE` | Configuration value (stored as CLOB) |
| `AGENT` | Logical name of the Select AI agent(Available in tools) |
| `VALUE` | Configuration value (stored as `CLOB`) |
| `AGENT` | Logical agent name used to scope configuration |

Configuration entries are uniquely identified by the combination of `KEY` and `AGENT`.

---

### Writing Configuration Entries

Configuration values are written during agent installation or setup.
Only explicitly provided values are persisted; agents apply internal defaults when values are absent.

---
Configuration values are written during installation or setup.
Only explicitly provided values are persisted.

### Example Configuration Entries

#### NL2SQL Data Retrieval Agent

```sql
INSERT INTO SELECTAI_AGENT_CONFIG ("KEY", "VALUE", "AGENT")
VALUES ('ENABLE_RESOURCE_PRINCIPAL', 'YES', 'NL2SQL_DATA_RETRIEVAL_AGENT');
Expand All @@ -119,51 +145,61 @@ VALUES ('CREDENTIAL_NAME', 'MY_DB_CREDENTIAL', 'NL2SQL_DATA_RETRIEVAL_AGENT');

### JSON-Based Configuration Input

Agent installers may accept configuration as JSON input.
Agent installers may also accept configuration as JSON input:

Example:

```{
```json
{
"use_resource_principal": true,
"credential_name": "MY_DB_CREDENTIAL"
}
```

The installer parses the JSON and stores the relevant values in SELECTAI_AGENT_CONFIG.
The installer parses the JSON and stores relevant values in `SELECTAI_AGENT_CONFIG`.

### Reading Configuration at Runtime

At runtime, agents read their configuration from SELECTAI_AGENT_CONFIG and consume it as structured JSON. This allows configuration changes without modifying agent code.
At runtime, agents read values from `SELECTAI_AGENT_CONFIG` and consume them as structured JSON. This allows configuration changes without modifying agent code.

---

## Supported Use Cases

This framework can be used to build Select AI agents for:
This framework can be used to build Select AI Agent solutions for:

- Natural language to SQL (NL2SQL)
- Data retrieval and analytics
- Database administration and monitoring
- Operational workflows
- Custom enterprise automation

Agents can target database-only workflows, service integrations, or mixed enterprise use cases.
- OCI and enterprise operational workflows
- Custom automation with database-native controls

---

## Compatibility and Release Support

Select AI capabilities vary by database release. For details on supported features across Autonomous Database and compatible non-Autonomous Database releases, refer to the official Select AI Capability Matrix:
Select AI capabilities vary by database release. For supported features across Autonomous Database and compatible non-Autonomous releases, see the Select AI Capability Matrix:

https://docs.oracle.com/en/database/oracle/oracle-database/26/saicm/select-ai-capability-matrix.pdf

---

## Database Inspect Agent Demo Video

Watch this demo video to see the Database Inspect agent in action:

[![Database Inspect Agent Demo Thumbnail](https://objectstorage.eu-frankfurt-1.oraclecloud.com/p/ZF8hVJGpN1RJDwOh1ZkRVJjKgzKdqYKuEDtIT1mekE18seU23DtZVbP5mHyJXQWm/n/dwcsdev/b/select_ai_agent_demo/o/Datbase_Inspect.png)](https://objectstorage.eu-frankfurt-1.oraclecloud.com/p/DqsbbE15HSHZv6LgeqS6yqi4OmxLaeO3ykwZ-DaKAM7yOZ3VDFWjafKk1ghh1k7x/n/dwcsdev/b/select_ai_agent_demo/o/DATABASE_INSPECT_AGENT.mp4)

[▶ Watch Database Inspect Agent Demo](https://objectstorage.eu-frankfurt-1.oraclecloud.com/p/DqsbbE15HSHZv6LgeqS6yqi4OmxLaeO3ykwZ-DaKAM7yOZ3VDFWjafKk1ghh1k7x/n/dwcsdev/b/select_ai_agent_demo/o/DATABASE_INSPECT_AGENT.mp4)

The demo walks through a practical inspection workflow where the agent accepts natural-language prompts, identifies relevant database objects, retrieves metadata and dependencies using tools, and returns structured responses that help developers analyze and troubleshoot code faster.

---

## Getting Started

1. Run the Tools layer scripts to install and register Select AI tools.
2. Run the Agent layer scripts to create sample tasks, agents, and teams.
3. Customize existing agents or create new ones by composing available tools.
4. Extend the framework by adding new tools and agent definitions.
3. Validate with test prompts for your selected AI profile.
4. Customize existing templates or build new agents by composing available tools.

---

Expand All @@ -177,11 +213,10 @@ This repository is intended for:
- Platform teams
- AI practitioners working with Oracle Autonomous AI Database

Anyone looking to build secure, database-native AI agents using Select AI can use this repository as a starting point.

---

## License

This project is licensed under the **Universal Permissive License (UPL), Version 1.0**.
This project is licensed under the Universal Permissive License (UPL), Version 1.0.
See: https://oss.oracle.com/licenses/upl/
Copyright (c) 2026 Oracle and/or its affiliates.
43 changes: 42 additions & 1 deletion autonomous-ai-agents/database_inspect/README.md
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## Overview

Select AI Inspect is an AI-powered inspection tool built using the **Select AI Agent** framework. It enables users to explore, understand, and interact with database objects and their metadata using natural language.
Select AI Inspect is an AI-powered database inspection tool built using the **Select AI Agent** framework. It enables users to explore, understand, and interact with database objects and their metadata using natural language.

For definitions of **Tool**, **Task**, **Agent**, and **Agent Team**, see the top-level guide: [README](../README.md#simple-agent-execution-flow).

### Use Cases

Expand Down Expand Up @@ -44,6 +46,37 @@ Users may define the inspection scope either at the individual object level or a

---

## Prerequisites

- Oracle Autonomous AI Database (26ai recommended)
- Select AI and `DBMS_CLOUD_AI_AGENT` enabled
- `ADMIN` or equivalent privileged user for installation
- A Select AI profile created with `DBMS_CLOUD_AI.CREATE_PROFILE`

---

## Installation

Before running installation commands:

1. Clone or download this repository.
2. Open a terminal and change directory to `autonomous-ai-agents/database_inspect`.
3. Choose one execution mode:
- SQL*Plus/SQLcl: run script files directly with `@script_name`.
- SQL Worksheet (Database Actions or other SQL IDE): open the `.sql` file and run/paste its contents.
4. Uploading scripts to `DATA_PUMP_DIR` is not required for these methods.

Run as `ADMIN` (or another privileged user):

```sql
sqlplus admin@<adb_connect_string> @database_inspect_tool.sql
sqlplus admin@<adb_connect_string> @database_inspect_agent.sql
```

You can also execute the contents of `database_inspect_tool.sql` and `database_inspect_agent.sql` in SQL Worksheet.

---

## Architecture Overview

Run `database_inspect_tool.sql` to install `DATABASE_INSPECT` package and tools
Expand Down Expand Up @@ -193,3 +226,11 @@ This schema includes more than 10 tables, such as customers, products, orders, a
4. Explain what the CHECKOUT_PKG.reprice_order procedure is used for, including its purpose, parameters and business rules.
5. Can you write and run a test script for the calc_tax_amount function to verify the results and check for any bugs?
6. When I call the calc_tax_amount function, for state_code = 'CA' (rate 0.0825), calc_tax_amount(10.01, 'CA') returns 0.82, but it should return 0.83. Please debug the function and show me the exact code that needs to be fixed.

---

## License

Universal Permissive License (UPL) 1.0
https://oss.oracle.com/licenses/upl/
Copyright (c) 2026 Oracle and/or its affiliates.
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