Part V AI Agents

This chapter provides information on creating, testing, deploying and monitoring agents in your workspace.

Agents are end-to-end agentic applications. Agents are defined through a graph of steps represented by nodes of different types (triggers, agents, guardrails, or tools). Agents can be defined through a no-code visual flow builder and through code via third-party libraries, such as LangGraph.

Oracle AI Data Platform Workbench offers multiple tool templates that can be configured to access your data and fit your use cases. The supported tools are:
  • Custom Tool: The Custom Code tool lets agent developers extend AI Data Platform with their own Python code. You package your tool implementation as a ZIP file, upload it to your workspace, and configure it. The agent calls your code as a tool, with parameters supplied by the LLM at runtime.
  • HTTP: The HTTP Request tool lets your agent call any HTTPS REST API. You configure the request, including method, URL, headers, query parameters, request body, authentication, and optionally, a response optimization step. The agent then invokes the endpoint at runtime. The HTTP request tool is available in both the visual builder and the code builder. In the code builder, the tool is configured through the aidpUtils Python library.
  • Prompt: The prompt tool allows the AI developer to define a parametrized prompt that can be issued to an LLM for their choice. Common use cases for a prompt tool include email drafting tasks, translation tasks, style conversion, git commit message, and code explanations.
  • Remote MCP Server: Agent developers can connect their agents to remote model context protocol (MCP) servers using the Remote MCP Server tool.
  • RAG: The RAG tool lets agents pull relevant external knowledge before generating a response. In AI Data Platform Workbench, the RAG tool queries a knowledge base (23ai Vector Search) and retrieves semantically relevant document chunks. Those chunks are then passed to the agent for response generation.
  • SQL: The SQL tool enables agents to execute SQL queries against structured data sources registered via external catalogs, such as Oracle Autonomous AI Lakehouse, Oracle Autonomous AI Transaction Processing, or Oracle AI Database. The tool is intended for scenarios where the SQL queries are predefined and can be parametrized. The objective is to let an agent assign values to the parameters. This tool is not an NL2SQL tool that generates a SQL query based on a natural language prompt.

    Note:

    The SQL tool only performs queries against data in an external catalog. It does not support data stored in a standard catalog.

Note:

You must attach an AI Compute to your agent before you can test a system tool. If no compute is attached, the Test tab is disabled.

Creating agents in AI Data Platform Workbench generates an agent artifact file (.aflow) in the workspace folder that you select. This file can’t be modified.