Configure the Agentic AI Thinking Pattern

AI agents can use different thinking patterns for reasoning. Before you can create an AI agent, you need to create the thinking pattern the agent will use.

  1. In the left navigation pane, select Projects.
  2. Select the Simple Expense Approval project that you created.
  3. In the left navigation pane, select AI Agents AI Agent icon.
  4. In the Agent patterns box, click Add.

    The Create pattern panel is displayed.

  5. In Create pattern, configure required fields for your pattern:

    Create pattern screen with fields for name ("ReAct for Simple Expense Approval Agent"), identifier ("REACTPATTERN_FOR_AGENTS"), pattern recipe ("ReAct" selected), and an empty description box. At the bottom are Cancel and Add buttons.

    • Name: Name for the thinking pattern. For our tutorial, enter the name ReAct for Simple Expense Approval Agent. Currently, there are two patterns available in Oracle Integration. ReAct and Plan and Execute. You might want to identify your pattern with those names for easier understanding.
    • Identifier: Specify REACTPATTERN_FOR_AGENTS.
    • Pattern recipe:

      Value sent to the LLM as a system prompt to define how the agent reasons. Select the ReAct pattern for the tutorial. A pattern recipe is implemented as an integration that contains the thinking pattern for the AI agent. Oracle Integration ships with two ready-to-use patterns:

      • ReAct: Reasoning and Action. The AI agent alternates between reasoning and acting. The AI agent updates its plans based on new information that it learns. The AI agent prompts the LLM for the next action, acts, learns, and then repeats.
      • Plan and Execute: The AI agent prompts the LLM to generate a complete plan, then executes that plan step by step.
  6. Click Create.
    The Pattern details page is displayed.
  7. Configure the Pattern and LLM.
    1. Leave the following fields with the default number:
      • Max iterations: Leave Max iterations as the default number. This limits the number of reasoning and action steps the agent can take before providing a final answer or exiting. This field prevents infinite loops.
      • Temperature: Leave as the default number. A higher temperature means the LLM can be more creative in its thinking.
    2. In the Guidelines box, enter the following guidelines for the ReAct thinking pattern:

      Screen for the ReAct for Simple Expense Approval Agent pattern, showing max iterations set to 10 and temperature set to 0. The guidelines box explains how to follow the ReAct agent pattern with steps for think, action, observe, and repeat. On the right, LLM Connections displays "LLM_Connection."

      Strictly follow all guidelines. It is mandatory that you reflect on all guidelines provided.

You should follow the ReAct agent pattern (Reason + Act) when generating a response.
Pattern:
Think - using your internal reasoning.
Action - Invoke tools to get information, including additional human input.
Observe - Process tool response using your internal reasoning.
Repeat - Repeat until finished.

At each step you provide your think, action, and observe rationale.

You should always attempt to get the most relevant information based on tool use.

Do not guess or infer tool arguments except for the following reasons:

You are given specific guidance by the tool on inferring a tool argument.

You have previous context to make a reasonable assumption.
    3. Click Save.
    4. Under LLM Connections, click Edit icon to configure the LLM connection.
      A new LLM connection is created and is displayed.
    5. Configure connection information for your LLM.

      LLM_Connection configuration page. The left displays properties for the connection, including Base URL "https://api.openapi.com ," model "gpt-4o-mini," and a masked API key for API Key Based Authentication. Configuration progress shows 100%. On the right, a "Save changes?" panel indicates there are no active integrations, but the inactive integration "ReAct Pattern Intg (1.0)" is configured. "Close" and "Save" buttons appear at the bottom.

      • Base URL: URL to connect to your LLM. For example: https://api.openai.com.
      • Model: Model to use for your AI agent. For example: gpt-4o-mini
      • API Key Based Authentication: Specify your API key to connect to the LLM.
    6. Click Test to test that your connection works, then click Save.

      When you save, Oracle Integration creates an integration that implements the ReAct pattern called ReAct Pattern Intg. This system-created integration is automatically activated for the agent.

  8. Click Back Back icon to return to the Pattern details page.
  9. Click Back Back icon to return to the AI Agents page.
  10. Activate the thinking pattern. The pattern must have the status Active before you can specify it in your agent.
    1. In the Agent patterns section, next to ReAct for Simple Expense Approval agent, click Actions Actions icon, and select Activate.

Once your thinking pattern is created for your AI agent, Oracle Integration automatically creates two integrations in your project: ReAct Pattern Intg and Get Tool Data. Select the Integration section to see these integrations. These integrations are system-generated and are required for your AI agent. You don't need to change or edit them.

The LLM connection is also automatically created for you.


Integrations panel with three integrations: "Get Tool Data," "ReAct Pattern Intg," and "Auto Approve Expense Report." Each integration is version 1.0.0 and configured, with "Auto Approve Expense Report" marked as active. The Connections panel on the right lists "LLM_Connection" (invoke), "REST Connection" (trigger), and "REST_TRIGGER_FOR_AUTO_APPROVAL" (trigger), all configured. There are search and navigation options at the top.

Next step: Add and Configure the AI Agent