minor updates

Author: changliu2

articles/ai-foundry/how-to/develop/agent-evaluate-sdk.md

@@ -46,7 +46,7 @@ pip install azure-ai-evaluation
 
 Agents typically emit messages to interact with a user or other agents. Our built-in evaluators can accept simple data types such as strings in `query`, `response`, `ground_truth` according to the [single-turn data input requirements](./evaluate-sdk.md#data-requirements-for-built-in-evaluators). However, to extract these simple data from agent messages can be a challenge, due to the complex interaction patterns of agents and framework differences. For example, as mentioned, a single user query can trigger a long list of agent messages, typically with multiple tool calls invoked. 
 
-As illustrated in the example, we enabled agent message support specifically for these built-in evaluators to evaluate these aspects of agentic workflow. These evaluators take `tool_calls` or `tool_definitions` as parameters (unique to agents.
+As illustrated in the example, we enabled agent message support specifically for these built-in evaluators to evaluate these aspects of agentic workflow. These evaluators take `tool_calls` or `tool_definitions` as parameters unique to agents.
 
 | Evaluator       | `query`      | `response`      | `tool_calls`       | `tool_definitions`  | 
 |----------------|---------------|---------------|---------------|---------------|
@@ -62,7 +62,7 @@ For `ToolCallAccuracyEvaluator`, either `response` or  `tool_calls` must be prov
 
 We will demonstrate some examples of the two data formats: simple agent data, and agent messages. However, due to the unique requirements of these evaluators, we recommend referring to the [sample notebooks](#sample-notebooks) which illustrate the possible input paths for each evaluator.  
 
-As with other [built-in AI-assisted quality evaluators](./evaluate-sdk.md#performance-and-quality-evaluators), `IntentResolutionEvaluator` and `TaskAdherenceEvaluator` output a likert score (integer 1-5) where the higher score is better the result. `ToolCallAccuracyEvaluator` output the passing rate of all tool calls made (a float between 0-1) based on user query. To further improve intelligibility, all evaluators accept a binary threshold and output two new keys. For the binarization threshold, a default is set and user can override it. The two new keys are:
+As with other [built-in AI-assisted quality evaluators](./evaluate-sdk.md#performance-and-quality-evaluators), `IntentResolutionEvaluator` and `TaskAdherenceEvaluator` output a likert score (integer 1-5; higher score is better). `ToolCallAccuracyEvaluator` outputs the passing rate of all tool calls made (a float between 0-1) based on user query. To further improve intelligibility, all evaluators accept a binary threshold and output two new keys. For the binarization threshold, a default is set and user can override it. The two new keys are:
 
 - `{metric_name}_result` a "pass" or "fail" string based on a binarization threshold.
 - `{metric_name}_threshold` a numerical binarization threshold set by default or by the user
@@ -244,11 +244,7 @@ print(result)
 
 #### Converter support
 
-Transforming agent messages into the right evaluation data to use our evaluators can be a nontrivial task. If you use [Azure AI Agent Service](../../ai-services/agents/overview.md), however, you can seamlessly evaluate your agents via our converter support for Azure AI agent threads and runs. Here's an example to create an Azure AI agent and some data for evaluation:
-
-```bash
-pip install azure-ai-projects azure-identity
-```
+Transforming agent messages into the right evaluation data to use our evaluators can be a nontrivial task. If you use [Azure AI Agent Service](../../ai-services/agents/overview.md), however, you can seamlessly evaluate your agents via our converter support for Azure AI agent threads and runs. Here's an example to create an Azure AI agent and some data for evaluation. Separately from evaluation, Azure AI Agent Service requires `pip install azure-ai-projects azure-identity` and an Azure AI project connection string and the supported models.
 
 #### Create agent threads and runs
 ```python
@@ -429,6 +425,7 @@ Now you're ready to try a sample for each of these evaluators:
 - [Intent resolution](https://aka.ms/intentresolution-sample)
 - [Tool call accuracy](https://aka.ms/toolcallaccuracy-sample)
 - [Task adherence](https://aka.ms/taskadherence-sample)
+- [Response Completeness](https://aka.ms/rescompleteness-sample)
 - [End-to-end Azure AI agent evaluation](https://aka.ms/e2e-agent-eval-sample)