{"id":53056,"date":"2025-04-24T09:58:27","date_gmt":"2025-04-24T09:58:27","guid":{"rendered":"https:\/\/www.bravent.net\/?p=53056"},"modified":"2025-04-24T10:04:08","modified_gmt":"2025-04-24T10:04:08","slug":"intelligent-agents-tailored-innovation-to-maximize-your-potential","status":"publish","type":"post","link":"https:\/\/www.bravent.net\/en\/news\/intelligent-agents-tailored-innovation-to-maximize-your-potential","title":{"rendered":"Intelligent Agents: Tailored Innovation to Maximize Your Potential"},"content":{"rendered":"

Intelligent Agents<\/strong><\/h2>\n

There are various ways to leverage the power of AI<\/strong> and integrate it into our daily tasks.<\/p>\n

Equally, depending on a company’s specific needs or use cases, we can utilize tools that allow us to create or deploy intelligent agents<\/strong> with minimal coding or even automatically.<\/p>\n

However, for more complex business logic and integration with different systems, custom development<\/strong> will be necessary.<\/p>\n

To get started, let\u2019s explore how to create AI agents that can help us optimize our work.<\/p>\n<\/div>[vc_empty_space][vc_single_image image=”53059″ img_size=”full” alignment=”center”][vc_empty_space]<\/div><\/div><\/div><\/section>

4 ways to create AI Agents to optimize our work processes<\/strong><\/h2>\n
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  1. Microsoft 365 Copilot<\/a> allows us to integrate AI into our Microsoft 365 suite, including applications like Excel, Outlook, PowerPoint, OneDrive, and OneNote.Furthermore, we can enhance Copilot through connectors or plugins, which enable us to perform more customized tasks or queries based on our specific business logic.<\/li>\n
  2. There are several ready-to-use agents available within Microsoft 365<\/a>.Another notable feature is the interpreter agent<\/a>, which, when used with Microsoft Teams, allows us to listen in real-time to a speaker using their native language, providing an audio translation in a language of our choice.<\/li>\n
  3. Copilot Studio<\/a> represents the evolution of virtual agents. It enables us to create intelligent chatbots that leverage generative AI for improved conversational experiences.E.g., we can connect multiple data sources and use low-code solutions through Power Automate to automate tasks, create triggers, and configure actions that result in chatbots seamlessly integrated with our knowledge base and generative AI<\/a>.<\/li>\n
  4. Custom developments<\/a> can be tailored to meet the unique needs of each company.Certainly, depending on the complexity of the situation and specific business logic, the standard functionalities of Copilot or Copilot Studio may not always fulfill the actual requirements of a business.<\/li>\n<\/ol>\n

    Want to see an example?<\/strong><\/h2>\n

    Consider a scenario in which we create a conversational chatbot designed to assist with bidding processes. This chatbot can handle tasks such as attaching a request for proposal (RFP), automatically extracting key data, comparing that data against the company’s internal rules and policies, and determining whether we meet the qualifications needed to pursue the RFP. Additionally, it could incorporate a machine learning algorithm trained on the company\u2019s historical responses. This setup would not only help estimate the likelihood of winning the RFP but also assist in creating the response while integrating it with the company’s systems and processes\u2014an example of a case that requires custom AI development.<\/p>\n

    Firstly, let’s take a closer look at some of the intelligent agents available.<\/p>\n<\/div>[vc_empty_space]<\/div><\/div><\/div><\/section>

    Copilot Studio and Copilot Studio RAG<\/strong><\/h2>\n

    Likewise, Copilot Studio<\/strong> can address a wide range of use cases, including the creation of conversational chatbots tailored to specific documents. Users can easily integrate their knowledge base into the AI using various connectors.<\/p>\n

    However, in some situations, indexing certain documents\u2014depending on their size or format\u2014may yield suboptimal results. Additionally, the default indexing performed by the tool is not customizable or improvable.<\/p>\n

    Therefore, for these specific use cases, it may be necessary to implement custom development<\/strong> or indexing within Copilot Studio RAG<\/strong>.<\/p>\n<\/div>[vc_empty_space]

    Differences between Copilot Studio and Copilot Studio RAG with Custom indexing<\/strong><\/h2>\n

    Given these points, Copilot Studio and Copilot Studio RAG<\/strong> with custom indexing are two approaches for implementing retrieval-augmented generation (RAG) systems<\/strong> using the Copilot Studio platform.<\/p>\n

    However, while both are built on the same technological foundation, they differ significantly in implementation and capabilities.<\/p>\n