Author / Simon Doy

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Posts by Simon Doy

I am an avid SharePoint enthusiast who works as an Independent SharePoint Consultant based in Leeds, United Kingdom. I am one of the organisers of the Yorkshire SharePoint User Group in the United Kingdom. I have been designing and building SharePoint solutions since 2006.
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by Simon Doy

Improving my approach: Using AI to Documenting Solution Designs for Development Teams


As we prepare for the next release of SmartFlo, I wanted to share a way that I use SmartFlo to help me get things done.

It has always been interesting and a challenge to be the person who leads the discussion with the customer and what they are looking for in terms of having a business process transformed or a system built for them. The most important part following the discussion is communicating with the development team on approach, pitfalls, and ideas. This often falls to the solution architect or business owner, who has to effectively communicate what they are looking for, design approaches, etc, to the development teams.

In this role, I explain what it is that we are building and why. I will pretty much always do some research, provide guidance as to how to do things, and what to watch out for so that the team are going to be taking a successful approach and hopefully not get blocked.

As part of this process, I need to explain how and the expectations of what the customer wants. Additionally, there are often a lot of other factors which make up what different stakeholders are looking for, and those will have been discussed with the pre-sales team. It is important that those discussion points are handed over successfully so that the delivery teams build the right thing.

Nine times out of ten, it comes down to communication and ensuring that the design and intention is clearly communicated and, most importantly, written down.

This allows the Dev team to ingest the design and approach in their own time and in a repeatable way.

Recently, I have been using SmartFlo to help me capture this information in a consistent and well-structured way.

The SmartFlo template that I have created has the following sections to capture the important information, such as security considerations, number of users, data size, areas of risk, concerns and more.

So why don’t we delve into what the template looks like and the process of creating this content?

To create the document, I simply have a meeting with myself and/or another member of the team, and we talk through the solution.

We talk through how we expect it to be built. We include what is being built and why. We talk about pitfalls and areas of concern. We really delve into the details so we can get as much information recorded in that meeting.

I then finish the meeting and run SmartFlo. This will create the document, which is then reviewed by me. I make any necessary updates, and then it’s ready to share with the team for feedback.

Here is an example of the output

Over time of using this process, the template has been refined further to help pick out areas which need their own section. Such as authentication or what licenses are required.

SmartFlo has dramatically reduced the time taken to write these documents, but it has also helped to improve the quality and consistency of what is produced.

Now with SmartFlo, what used to take 90 -120 minutes to write now takes 20-30 minutes!

Creating the template

Creating the SmartFlo template is straightforward. We take our standard company-branded template and create some sections to record the key information.

You can see this below.

We upload the template into SmartFlo and then use the following to describe the template.

For each of the sections, we tell SmartFlo how to fill out the template.

That is it, there is some good detail in the template and getting that right can take a couple of attempts, but it’s incredibly quick and easy to get one of these templates out.

What do you think?

I would love for you to benefit from tools such as this and improve how you get things done. To help, you can install and try out SmartFlo, by getting started and signing up for a 7-day trial.

Please let us know what you think!

by Simon Doy

Build Better Agent Experiences for your Customers with Copilot Studio and Topic Variables


Introduction

At iThink 365, we have been building AI Agents using both Microsoft Copilot Studio and Azure AI Foundry. The Microsoft Copilot Studio product is constantly evolving and improving. When chatting with people, I often find that people are not aware of some really useful features that help you to build better and more intuitive agents.

In this post, I wanted to share a couple of tips on how you can improve the conversation flow of Agents built in Microsoft Copilot Studio, making them more intuitive and easier for you and your customers to use.

Input Variables

Copilot Studio is able to have input variables which are scoped at the topic level. This incredible feature allows you to make use of your Copilot Studio LLM to discover and fill the input variables based on how you tell the LLM to identify the information that should populate the variable.

This capability is really powerful and can help take a lot of the heavy lifting of detection, transformation and capturing of information for each topic.

These input variables are configured for a topic using the Details tab. Within the details tab you have three tabs: Topic details, Input and Output. Use the Input tab to configure the inputs to the topic.

These variables allow you to capture key parameters and information that you need for the topic to function properly.

Let’s go through an example. In this example, we are processing a user’s leave request. This is achieved by creating the leave or holiday request topic. With this topic, you might have a start date and end date of the leave request. You also have a comment or reason for the leave request. By creating an input variable for each input, such as:

  • Leave start date
  • Leave end date
  • Holiday comments

The topic will then have each variable automatically populated based on the input from the user. Let’s take the input from the user.

“I would like to go on holiday with my family from the 1st August to 14th August.”

If we configure the topic input variables correctly, then this user prompt will equate to the following:

  • Leave start date => 1st August 2025
  • Leave end date => 14th Augst 2025
  • Holiday comments => Taking a holiday with family.

These input variables are powerful, and they help simplify the topics. Rather than having a topic with a set of questions that ask for more information from the user. They use the LLM and its knowledge to fill out the input variables or fill the slots. This means that the conversation with the agent is much more natural, and a request can be put together in natural language as a sentence rather than a series of one-word prompts for each part of the request.

The image below shows an example if you were not using the input variables. You can see all the questions that the user would be asked for each of the dates for the leave request, this is not going to give a smooth or conversational fill to the agent.

The input variables can be configured so that you can give feedback if a variable cannot be filled. An example is shown below.

Using this approach helps you guide your user with what information to provide to get the topic to work correctly.

Now that we have talked about input variables, lets talk about how we can use output variables to improve the responses that Copilot Agents provide to the users.

Output Variables

When we first started building agents in Copilot Studio, one of the challenges we had was

“How do you help the Agent to respond with the right information?”

You can use activities such as the message activity to output responses back to the user.

Well, fortunately Copilot Studio has “Output” variables which can be used to capture the key information that the topic should include. This will be used by the Agent and LLM to make a suitable response to the user as a conversation.

How do we use and configure the output variable?

The output variables are configured at the same place as the input variables by clicking on the details tab and choosing Output. Here, you can create multiple variables for the topic output and describe to the Copilot Studio Agent what is contained in the variable. This will help the Agent to come up with a suitable response for the user following the topic completion.

The creation of the output variable allows it to be set during the processing of the topic. For example, in our example, we might then fill the output variable with details of the leave request with the start date, end date, leave request comment, manager details and that it has been submitted for approval.

Using the Output variables gives a great way to control the information that should be given to the LLM so that it can use it to respond back to the user.

What is one of the key features when using Output variables is that you are not showing the raw data back to the user which could confuse the user as to what is important or not.

To help, lets take the following example, an agent that helps us creating a marketing campaign. This agent allows us to create a campaign over two weeks which builds up a story. The agent uses AI to generate the idea and we can ask it for the post ideas. Here, the agent returns a chunk of data. Now I used to output this via a message activity so that the agent had some content to respond back to the user in a more natural way. However, this means that you see a load of data coming back that is not nicely formatted and, as mentioned above, is confusing to the end user.

However, using the Output variables to capture that information, the output from the agent now looks like this.

There is no rubbish being output to the user instead, in the second example, we only see the result that the Agent displays, and this has been nicely formatted by the Agent’s LLM.

This is a much better experience for the user and leads to a conversational type flow from the agent to the flow, which feels nicer and more natural to interact with.

Conclusion

In this article, we explained how we can make use of key Copilot Studio Topic features, which allow us to use the power of Generative AI and LLMs to do the heavy lifting and detection of inputs. This helps us improve how our agents function, making them feel more natural and enhancing the conversational style of the agent when processing user requests and responding to them.