Intelligent Agents

There are various ways to leverage the power of AI and integrate it into our daily tasks.

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

However, for more complex business logic and integration with different systems, custom development will be necessary.

To get started, let’s explore how to create AI agents that can help us optimize our work.

Intelligent Agents: Tailored Innovation to Maximize Your Potential

4 ways to create AI Agents to optimize our work processes

Microsoft 365 Copilot 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.
There are several ready-to-use agents available within Microsoft 365.Another notable feature is the interpreter agent, 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.
Copilot Studio 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.
Custom developments 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.

Want to see an example?

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’s 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—an example of a case that requires custom AI development.

Firstly, let’s take a closer look at some of the intelligent agents available.

Copilot Studio and Copilot Studio RAG

Likewise, Copilot Studio 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.

However, in some situations, indexing certain documents—depending on their size or format—may yield suboptimal results. Additionally, the default indexing performed by the tool is not customizable or improvable.

Therefore, for these specific use cases, it may be necessary to implement custom development or indexing within Copilot Studio RAG.

Differences between Copilot Studio and Copilot Studio RAG with Custom indexing

Given these points, Copilot Studio and Copilot Studio RAG with custom indexing are two approaches for implementing retrieval-augmented generation (RAG) systems using the Copilot Studio platform.

However, while both are built on the same technological foundation, they differ significantly in implementation and capabilities.

On the one hand, Copilot Studio utilizes a predefined knowledge base and offers a standard solution. It enables companies to integrate search and answer generation capabilities based on their data.Moreover, this solution leverages advanced language models, such as GPT-4, combined with Azure Cognitive Search to provide accurate and relevant answers from indexed documents.Here, they benefits include:

Rapid deployment.
Integration with Microsoft Teams and other channels.
Reduced administrative burden.

On the other hand, Copilot Studio RAG with Custom Indexing, offers greater customization.Overall, this approach allows companies to tailor the solution to their specific needs by using custom indexing to index and train their documents.Also, it supports integration with various data sources, including SharePoint sites, public web pages via web scraping, JIRA, and more.Particularly, the standout features of this option are:

Custom interface and indexing.
Advanced integration capabilities.
Streamlined search processes.
Ongoing support.

What improvements do custom developments bring to Copilot Studio RAG?

Particulartly, custom developments enhance Copilot Studio RAG when greater precision is required in responses or when there is a need to index a larger volume of documentation than what Copilot Studio or Power Automate can accommodate.

In such cases, we recommend opting for custom development.

So, at Bravent, we create services in Azure to manage the Copilot Studio RAG system, giving us control over all operations within Copilot Studio.

Simultaneously, this ensures that the company has complete oversight of the information it indexes, the services it utilizes, and the specific actions it wants Copilot to perform in various scenarios.

Furthermore, by employing Azure Search, organizations can index a wide range of sources, including SharePoint, the internet, JIRA, and more.

Consequently, this allows companies to index diverse materials such as videos, PDFs with images, Excel spreadsheets containing complex tables, and other documents.

Additionally, thanks to advanced LVM models, these materials can be analyzed in detail, allowing for a deeper understanding of their content.

Where can I find information on custom developments in Copilot Studio RAG?

Lastly, information is available in Azure AI Search, which enables various types of analysis through features such as Azure AI Video Indexer, Azure AI Document Intelligence, and GPT-4.

To clarify, this platform allows the company to index diverse types of documentation, giving users control over how information is organized and accessed.

However, this functionality does not extend to Copilot Studio.

Conclusion

Choosing between Copilot Studio and Copilot Studio RAG customization ultimately depends on the specific needs of each company.

In summary, while both solutions are effective, Copilot Studio RAG with custom indexing provides more flexibility and tailored functionalities for organizations looking to meet specific requirements.

The standard solution provides quick and efficient implementation, while the customized option offers greater flexibility and adaptability to unique use cases, ensuring that the tool can evolve alongside business requirements.

For more details, you can contact us at Info@bravent.net