Fix terminology

Author: ecfan

articles/logic-apps/create-autonomous-agent-workflows.md

@@ -675,6 +675,6 @@ For this example, follow these steps:
 ## Related content
 
 - [AI agentic workflows in Azure Logic Apps](/azure/logic-apps/agent-workflows-concepts)
-- [Lab: Build your first autonomous agent workflow in Azure Logic Apps](https://azure.github.io/logicapps-labs/docs/logicapps-ai-course/build_autonomous_agents/create-first-autonomous-agent)
+- [Lab: Build your first autonomous agentic workflow in Azure Logic Apps](https://azure.github.io/logicapps-labs/docs/logicapps-ai-course/build_autonomous_agents/create-first-autonomous-agent)
 - [Azure Logic Apps limits and configuration](/azure/logic-apps/logic-apps-limits-and-config)
 - [Azure OpenAI Service quotas and limits](/azure/ai-services/openai/quotas-limits)

articles/logic-apps/includes/ai-action-parameter-values-runtime.md

@@ -2,7 +2,7 @@
 ms.service: azure-logic-apps
 ms.author: estfan
 author: ecfan
-ms.date: 11/18/2025
+ms.date: 02/18/2026
 ms.topic: include
 ---
 
@@ -18,4 +18,4 @@ This section describes the options for how your MCP server sources input paramet
 
 - User-provided inputs
 
-  You specify the parameter values during development. These values are typically hardcoded and stay the same across all interactions between the agent and the end user.
+  You specify the parameter values during development. These values are typically hardcoded and stay the same across all interactions between the agent loop and the end user.

articles/logic-apps/includes/best-practices-agent-workflows.md

@@ -2,59 +2,59 @@
 ms.service: azure-logic-apps
 ms.author: estfan
 author: ecfan
-ms.date: 10/18/2025
+ms.date: 02/18/2026
 ms.topic: include
 ---
 
-## Best practices for agents and tools
+## Best practices for agent loops and tools
 
-The following sections provide recommendations, best practices, and other guidance that can help you build better agents and tools.
+The following sections provide recommendations, best practices, and other guidance that can help you build better agent loops and tools.
 
-### Agents
+### Agent loops
 
-The following guidance provides best practices for agents.
+The following guidance provides best practices for agent loops.
 
-##### Prototype agents and tools with 'Compose' actions
+##### Prototype agent loops and tools with 'Compose' actions
 
-Rather than use actual actions and live connections to prototype your agent and tools, use [**Compose** actions](/azure/logic-apps/logic-apps-perform-data-operations#compose-action) to "mock" or simulate the actual actions. This approach provides the following benefits:
+Rather than use actual actions and live connections to prototype your agent loops and tools, use [**Compose** actions](/azure/logic-apps/logic-apps-perform-data-operations#compose-action) to "mock" or simulate the actual actions. This approach provides the following benefits:
 
 - **Compose** actions don't produce side effects, which make these actions useful for ideation, design, and testing.
 
-- You can draft and refine agent instructions, prompts, tool names and descriptions plus agent parameters and descriptions - all without having to set up and use live connections.
+- You can draft and refine agent loop instructions, prompts, tool names and descriptions plus agent parameters and descriptions - all without having to set up and use live connections.
 
-- When you confirm that your agent and tools work with only the **Compose** actions, you're ready to swap in the actual actions.
+- When you confirm that your agent loop and tools work with only the **Compose** actions, you're ready to swap in the actual actions.
 
 - When you switch over to the actual actions, you have to reroute or recreate your agent parameters to work with the actual actions, which might take some time.
 
 ##### Manage chat history context length
 
-The workflow agent maintains the chat history or *context*, including tool invocations, based on the current limit on the number of [tokens](/azure/ai-services/openai/overview#tokens) or messages to keep and pass into the model for the next interaction. Over time, the agent history grows and eventually exceeds your model's *context length* limit, or the maximum number of input tokens. Models differ in their context lengths.
+The agent loop maintains the chat history or *context*, including tool invocations, based on the current limit on the number of [tokens](/azure/ai-services/openai/overview#tokens) or messages to keep and pass into the model for the next interaction. Over time, the agent loop history grows and eventually exceeds your model's *context length* limit, or the maximum number of input tokens. Models differ in their context lengths.
 
-For example, **gpt-4o** supports 128,000 input tokens where each token has 3-4 characters. When the agent history approaches the model's context length, consider dropping stale or irrelevant messages to stay below the limit.
+For example, **gpt-4o** supports 128,000 input tokens where each token has 3-4 characters. When the agent loop history approaches the model's context length, consider dropping stale or irrelevant messages to stay below the limit.
 
-Here are some approaches to reduce your agent history:
+Here are some approaches to reduce your agent loop history:
 
 - Reduce the size of results from tools by using the [**Compose** action](/azure/logic-apps/logic-apps-perform-data-operations#compose-action). For more information, see [Tools - Best practices](#tools).
 
-- Carefully craft your agent instructions and prompts to control the model's behavior.
+- Carefully craft your agent loop instructions and prompts to control the model's behavior.
 
 - **Experimental capability**: You have the option to try chat reduction so you can reduce the maximum number of tokens or messages to keep in chat history and pass into the model.
 
-  A workflow agent has almost the same advanced parameters as the [Azure OpenAI built-in, service provider connector](/azure/logic-apps/connectors/built-in/reference/openai/), except for the **Agent History Reduction Type** advanced parameter, which exists only in the agent. This parameter controls the history that the agent maintains, based on the maximum number of tokens or messages.
+  The agent loop has almost the same advanced parameters as the [Azure OpenAI built-in, service provider connector](/azure/logic-apps/connectors/built-in/reference/openai/), except for the **Agent History Reduction Type** advanced parameter, which exists only on the agent action. This parameter controls the history that the agent loop maintains, based on the maximum number of tokens or messages.
 
   This capability is in active development and might not work for all scenarios. You can change the **Agent History Reduction Type** option to reduce the limit on tokens or messages. You then specify the numerical limit that you want.
 
   To try the capability, follow these steps:
 
-  1. On the designer, select the agent's title bar to open the information pane.
+  1. On the designer, select the agent action's title bar to open the information pane.
   1. On the **Parameters** tab, find the **Advanced parameters** section.
   1. Check whether the parameter named **Agent History Reduction Type** exists. If not, open the **Advanced parameters** list, and select that parameter.
   1. From the **Agent History Reduction Type** list, select one of the following options:
 
      | Option | Description |
      |--------|-------------|
-     | **Token count reduction** | Shows the parameter named **Maximum Token Count**. Specifies the maximum number of tokens in agent history to keep and pass into the model for the next interaction. The default differs based on the currently used model in Azure OpenAI Service. The default limit is **128,000**. |
-     | **Message count reduction** | Shows the parameter named **Message Count Limit**. Specifies the maximum number of messages in agent history to keep and pass into the model for the next interaction. No default limit exists. |
+     | **Token count reduction** | Shows the parameter named **Maximum Token Count**. Specifies the maximum number of tokens in agent loop history to keep and pass into the model for the next interaction. The default differs based on the currently used model in Azure OpenAI Service. The default limit is **128,000**. |
+     | **Message count reduction** | Shows the parameter named **Message Count Limit**. Specifies the maximum number of messages in agent loop history to keep and pass into the model for the next interaction. No default limit exists. |
 
 ### Tools
 
@@ -66,11 +66,11 @@ The following guidance provides best practices for tools.
 
 - Both the tool name and description have character limits.
 
-  Some limits are enforced by the model in Azure OpenAI Service at run time, rather than when you save the changes in the agent in the workflow.
+  Some limits are enforced by the model in Azure OpenAI Service at run time, rather than when you save the changes in the agent loop in the workflow.
 
-- Too many tools in the same agent can have a negative effect on agent quality.
+- Too many tools in the same agent loop can have a negative effect on agent loop quality.
 
-  A good general guideline recommends that an agent includes no more than 10 tools. However, this guidance varies based on the model that you use from Azure OpenAI Service.
+  A good general guideline recommends that an agent loop includes no more than 10 tools. However, this guidance varies based on the model that you use from Azure OpenAI Service.
 
 - In tools, actions don't need to have all their inputs come from the model.
 
@@ -94,4 +94,4 @@ The following guidance provides best practices for agent parameters.
 
 - The name is the most important value for an agent parameter. Make sure the name is succinct and descriptive.
 
-- The agent parameter description provides useful and helpful context for the tool.
+- The agent parameter description provides useful and helpful context for the tool.

articles/logic-apps/includes/billing-agent-workflows.md

@@ -2,7 +2,7 @@
 ms.service: azure-logic-apps
 ms.author: estfan
 author: ecfan
-ms.date: 11/18/2025
+ms.date: 02/18/2026
 ms.topic: include
 ---
 
@@ -12,4 +12,4 @@ ms.topic: include
 
 - Consumption: Billing uses the pay-as-you-go model. Agent loop pricing is based on the number of tokens that each agent action uses and appears as Enterprise Units on your bill. For specific pricing information see [Azure Logic Apps pricing](https://azure.microsoft.com/pricing/details/logic-apps/).
 
-- Standard: Although agent workflows don't incur extra charges, AI model usage incurs charges. For more information, see the Azure [Pricing calculator](https://azure.microsoft.com/pricing/calculator/).
+- Standard: Although agentic workflows don't incur extra charges, AI model usage incurs charges. For more information, see the Azure [Pricing calculator](https://azure.microsoft.com/pricing/calculator/).

articles/logic-apps/includes/supported-models.md

@@ -2,30 +2,30 @@
 ms.service: azure-logic-apps
 ms.author: estfan
 author: ecfan
-ms.date: 11/18/2025
+ms.date: 02/18/2026
 ms.topic: include
 ---
 
 <a name="supported-models"></a>
 
-## Supported Azure OpenAI Service models for agent workflows
+## Supported Azure OpenAI Service models for agentic workflows
 
-The following list specifies the AI models that you can use with agent workflows:
+The following list specifies the AI models that you can use with agentic workflows:
 
 ### [Consumption (preview)](#tab/consumption)
 
-Your agent automatically uses one of the following Azure OpenAI Service models:
+Your agent loop automatically uses one of the following Azure OpenAI Service models:
 
 - gpt-4o-mini
 - gpt-5o-mini
 
 > [!IMPORTANT]
 >
-> The AI model that your agent uses can originate from any region, so data residency for a specific region isn't guaranteed for data that the model handles.
+> The AI model that your agent loop uses can originate from any region, so data residency for a specific region isn't guaranteed for data that the model handles.
 
 ### [Standard](#tab/standard)
 
-Your agent can use one of the following models:
+Your agent loop can use one of the following models:
 
 - gpt-5
 - gpt-4.1

articles/logic-apps/includes/troubleshoot-agent-workflows.md

@@ -2,35 +2,35 @@
 ms.service: azure-logic-apps
 ms.author: estfan
 author: ecfan
-ms.date: 09/08/2025
+ms.date: 02/18/2026
 ms.topic: include
 ---
 
 ## Troubleshoot problems
 
-This section describes guidance to help troubleshoot errors or problems that you might encounter when you build or run agent workflows.
+This section describes guidance to help troubleshoot errors or problems that you might encounter when you build or run agentic workflows.
 
 ### Review tool execution data
 
-The workflow run history provides useful information that helps you learn what happened during a specific run. For an agent workflow, you can find tool execution inputs and outputs for a specific agent loop iteration.
+The workflow run history provides useful information that helps you learn what happened during a specific run. For an agentic workflow, you can find tool execution inputs and outputs for a specific agent loop iteration.
 
 1. On the workflow menu, under **Tools**, select **Run history** to open the **Run history** page.
 
 1. On the **Run history** tab, in the **Identifier** column, select the workflow run that you want.
 
    The monitoring view opens to show the status for each step.
 
-1. Select the agent that you want to inspect. To the right side, the **Agent log** pane appears.
+1. Select the agent action that you want to inspect. To the right side, the **Agent log** pane appears.
 
    This pane shows the agent log, including tool executions during the interaction.
 
 1. To get tool execution data at a specific point, find that point in the agent log, and select the tool execution reference, for example:
 
    :::image type="content" source="media/troubleshoot-agent-workflows/tool-reference-links.png" alt-text="Screenshot shows agent log and selected tool execution link." lightbox="media/troubleshoot-agent-workflows/tool-reference-links.png":::
 
-   This action moves you to the matching tool in monitoring view. The agent shows the current iteration count.
+   This action moves you to the matching tool in monitoring view. The agent action shows the current iteration count.
 
-1. In monitoring view, select the agent or the action with the inputs, outputs, and properties that you want to review.
+1. In monitoring view, select the agent action or the action with the inputs, outputs, and properties that you want to review.
 
    The following example shows a selected action for the previously selected tool execution:
 
@@ -40,22 +40,22 @@ The workflow run history provides useful information that helps you learn what h
 
    - Input messages passed into the model.
    - Output messages returned from the model.
-   - Tools that the model asked the agent to call.
+   - Tools that the model asked the agent loop to call.
    - Tool results that passed back into the model.
    - Number of tokens that each request used.
 
 1. To review a different agent loop iteration, in the agent, select the left or right arrow.
 
 ### Logs in Application Insights
 
-If you set up Application Insights or advanced telemetry for your workflow, you can review the logs for agent events, like any other action. For more information, see [Enable and view enhanced telemetry in Application Insights for Standard workflows in Azure Logic Apps](/azure/logic-apps/enable-enhanced-telemetry-standard-workflows).
+If you set up Application Insights or advanced telemetry for your workflow, you can review the logs for agent loop events, like any other action. For more information, see [Enable and view enhanced telemetry in Application Insights for Standard workflows in Azure Logic Apps](/azure/logic-apps/enable-enhanced-telemetry-standard-workflows).
 
 ### Model maximum context length exceeded
 
 If your agent's log history exceeds the model's *context length*, or the maximum number of input tokens, you get an error that looks like the following example:
 
 **This model's maximum context length is 4097 tokens. However, you requested 4927 tokens (3927 in the messages, 1000 in the completion). Please reduce the length of the messages or completion.**
 
-Try reducing the limit on the number of tokens or messages that your agent keeps in log and passes into the model for the next interaction. For this example, you might select **Token count reduction** and set **Maximum Token Count** to a number below the error's stated maximum context length, which is **4097**.
+Try reducing the limit on the number of tokens or messages that your agent loop keeps in the log and passes into the model for the next interaction. For this example, you might select **Token count reduction** and set **Maximum Token Count** to a number below the error's stated maximum context length, which is **4097**.
 
 For more information, see [Manage chat history context length](#manage-chat-history-context-length).