In today’s fast-moving AI landscape, businesses are racing to embed conversational intelligence and automation into every customer touchpoint. However, building a reliable and scalable agent from scratch remains complex and time-consuming. Developers tell us they need a streamlined way to map diverse user intents, craft accurate responses, and support global audiences without wrestling with ad-hoc integrations. At the same time, rising expectations around data privacy and compliance introduce yet another layer of overhead.

To meet these challenges, today, we’re excited to announce a suite of powerful new tools and templates designed to help developers build intelligent agents faster than ever with our Azure AI Language service. Working together with Azure AI Agent Service, whether you’re triaging user intents, serving up precise answers, or translating content on the fly, our latest releases have you covered. Our latest releases include three ready-to-use agent templates and MCP server, enhanced Conversational Language Understanding (CLU) and Custom Question Answering (CQA) with an all-new authoring experience in Azure AI Foundry portal, updated conversational agent accelerator project, and strengthened privacy controls in Personally Identifiable Information (PII) detection service.

New Agent Templates

We are releasing three agent templates in Azure AI Agent Service catalog to bootstrap developers to address complex conversational scenarios efficiently: intent routing agent, exact question-answering agent and text translation agent. Each of these templates includes sample code available on GitHub to set up agents powered by core capabilities in Azure AI Language and Translator.

Figure 1: New agent templates available in agent catalog

1. Intent routing agent
   Leverage the combined power of our Custom Language Understanding (CLU) and Custom Question Answering (CQA) products. This template creates an agent that automatically detects which pre-defined business intent a user query maps to or returns the exact answer verbatim via CQA. It gives you fully predictable and controllable intent routing with no custom model training required. You can further extend the capabilities of this agent based on your needs. For example, add additional Knowledge to the agent to handle non-critical and unpredictable user questions through RAG, or connect with other agents to route the user query based on identified intents. Check out the GitHub repo for more info about intent routing agent.
2. Exact question-answering agent
   Focused solely on delivering verbatim answers from your curated knowledge base in CQA, this template is perfect for creating agent for FAQ bots, support portals, and any scenario where precision matters above all else. Similar to intent routing agent, you can enhance the exact question-answering agent with additional Knowledge to handle a wider range of user questions through RAG, improving traffic coverage and customer satisfaction. Check out the GitHub repo for more info about exact question-answering agent.
3. Text translation agent
   Integrate text translation seamlessly into your agent’s workflow with Azure AI Translator. This template facilitates multilingual support through straightforward agent setup, enabling your agent to communicate with customers in their preferred language and manage translation requests across various languages with high accuracy. Check out the GitHub repo for more info about text translation agent.

MCP Server with PII and Translator Tools

In addition to the agent templates, we are also announcing our new language MCP server with built-in core Language service capabilities as tools. This first release includes PII detection and translation tooling, allowing developers to easily integrate it with any agents. Check out its source code and more details in the GitHub repo.  

CLU and CQA Enhancements

To empower the new agent templates, we continued enhancing our CLU and CQA capabilities and experience:

• LLM-based intent detection in CLU
  Conversational Language Understanding (CLU) service powers intent detection and entity extraction that can be customized for various business. In addition to the traditional model training experience optimized for extreme high accuracy and low latency needs, CLU now also provides a new option that utilizes Azure OpenAI models to detect user intents. No additional training steps, datasets, or fine-tuning required. Simply define your intents and quick deploy.

Figure 2: Two CLU deployment options available in Azure AI Foundry portal

• New query reference settings in CQA
  Conversational Question Answering (CQA) service delivers highly precise responses from your pre-defined question-answer pairs, ensuring users receive the exact information your business requires. To further improve the question understanding and configurability, we are introducing “queryPreferences” property in the CQA API, with the support of new query matching policy, semantic ranker and also the classic ranker used in QnA Maker to support the needs from our QnA Maker customers to migrate to CQA. All these new features will be available at the end of this week.
• New authoring experience in Azure AI Foundry portal
  We are introducing CLU and CQA authoring experience in Azure AI Foundry portal. Regardless of whether you are using Azure AI Foundry resource, the AI Hub resource or Azure AI Language resource, you can now create Custom Question Answering (CQA) task in your Foundry projects to manage your question-answer pairs, and enjoy all the above new capabilities too! By the end of this week, you will also be able to create Conversational Language Understanding (CLU) task in Foundry projects to manage your intents and entities. No longer need to switch back and forth between the Foundry portal and Language Studio to manage different projects.

Figure 3: New CQA authoring experience in Azure AI Foundry portal

Updated Accelerator Project for Conversational Agent

We know getting started has never been simpler. To empower developers to make the best use of those new agent templates, an updated accelerator project for “Build your conversational agent” template will be available by the end of this week. The refreshed accelerator project will demonstrate how you can use the intent routing agent in an end-to-end solution from front-end to backend with sample data for testing. To access the project for the update at the end of this week, you can visit the Templates page in your Foundry project or check out the source code directly in the GitHub repo.

Figure 4: "Build your conversational agent" template in the Foundry portal

Enhanced Privacy Controls with Text PII Detection

When building agents with large language models, safeguarding user data is paramount. With today’s announcement, we are introducing several new capabilities to our Text PII Detection service to meet our customers’ needs with more customizability and entity/language coverage:

• Support for PII redaction in scanned PDF documents. The document support in PII redaction allows you to provide a document file and get the redacted file in return. In addition to .docx, .txt and text PDF file, you can now also provide a scanned file in .pdf for redaction. For more information how to use the native document support, see Detect and redact Personally Identifying Information in native documents (preview).
• Support for custom synonyms of PII detection entities. Now you can use “synonyms” property in the API call to define your own synonyms for a PII entity to achieve better detection rate.
• Support for exclusion of specific entity values from the detection. Use “ValueExclusionPolicy” property to specify words and terms that you want to exclude from the PII detection.
• Extended context window span limit for rule-based entities detection. The context window span is the length of the continuous data interval (or “chunks”) within your input text that the service internally takes at once for detecting the entities. We’ve extended it for rule-based entities to 500 characters to match the span used by our model-based detectors, ensuring the consistent detection behavior across all entities. For other service limits, including the maximum characters of input text, see Data limits for Language service features.
• Support new entity type, Date of Birth. For all supported entities, see Supported Personally Identifiable Information (PII) entity categories.
• Enhanced capabilities in Text PII container:
  • Support custom regex in Text PII container. Only available in our container offering, now you can define your own regular expressions directly within the Text PII container to catch any patterns you care about.
  • By the end of this month, we’ll also support more new languages, Chinese, Japanese, Korean and Thai, in PII container to keep the parity on the language support between the cloud service and the container.

We can’t wait to see the innovative agents you’ll build with these new capabilities. Let us know what you create, share your feedback, and stay tuned for even more enhancements coming soon!

Resources:

• Azure AI Language
• Azure AI Translator
• Azure AI Agent Service
• Intent routing agent
• Exact question-answering agent
• Text translation agent
• MCP server with PII detection and translator
• Conversational Language Understanding (CLU)
• Custom Question Answering (CQA)
• “Build your conversational agent” accelerator project
• Personally Identifiable Information (PII) detection