While building chatbots using Bot Framework Composer I often encoutered the problem that I needed to store properties and use them within my dialogs for different purposes. Within the Bot Framework SDK, I would most probably use the state to store information which is not only valuable within a single dialog or turn, but for the whole conversation. The equivalent of the state in Composer is called “memory” which can be described as the bot’s mind or brain.

As a Bot Framework enthusiast, I was looking for ways how to speed up routines in my daily tasks. Therefore I decided to publish some nice little helpers together with my friends from the Bot Framework Community to speed up tasks when working on a bot a bit while working with VS Code.

This post covers all the great announcements from this years Microsoft Build conference 2020

A step-by-step tutorial for building a Microsoft Teams messaging extension using the Microsoft Bot Framework to handle task management (To Do and Planer) directly within conversations!

First part of a blo series dealing with Microsoft Bot Framework Composer. This post is all about how to handle Adaptive Cards right

If you are building bots which are not used - you are not building bots! So analyze your bot’s performance with these little tricks

I wanted to automate the creation of my bots and the necessary services a bit. Therefore, I first took a look at what can be done in order to save time when working with LUIS. This post may be turning out to be the first of a many to come series about automating Bot Framework development and deployment

I am delighted so say, that I got accepted for this year’s SharePoint conference 2020 in Las Vegas. The event will take place from 17th to 22nd of May in total. Standard sessions will be presented May 19-21 and full day workshops will take place May 17, 18 and 22. This conference is a must-see if you want to learn anything about Office 365, SharePoint and OneDrive as there will be more than 150 speakers (Microsoft employees as well as industry-leaders and MVPs), more than 200 sessions (ranging from development focus to targeting IT pros and business users) and more than 20 full-day workshops, which allow you to get a deep dive into one specific topic.

Problem Statement When developing bots using the Bot Framework SDK v4 for JavaScript you certainly want to develop your bots locally using TypeScript, instead of doing the whole development within the Azure poral using the online code editor. But what might be annoying when doing that is that by default, you would need to run npm start for starting the bot. The only problem with that is, that you would need to restart the command again if you changed something in your code to be able that you can test the bot with the latest changes from the Bot Framework emulator.

Problem Statement Nowadays, it’s quite common to add a “virtual assistant” (a.k.a chatbot) to your public facing website. Almost every organization, especially the ones dealing with endusers (B2C market-oriented organizations) try to add value to their websites in form of a bot. And Microsoft has done a pretty good job, making it easy to build a bot, with little effort and almost no coding. But unfortunately, there is not a complete guide provided, on how to create the bot from the very first click until it can be deployed on to a public website.

The Background So I had this idea already a couple of months to release or better open-source a SharePoint Framework WebPart which is capable of bringing your Bot Framework conversations (= your bot) to SharePoint (Online). Now I finally had the chance of publishing my webpart to the official SPFx samples GitHub repo to make it available for everyone. So if you want to try it out and integrate it into your SharePoint tenant, just clone the repo, and follow the instructions mentioned here and you should be able to get it up and running in a couple of minutes.

The Starting Position Nowadays, styling is a hot topic. If it looks good - I’ll use it is often the mindset of users. So it’s very important to offer something very good-looking to be successful with what you do. And so it is with bots. The Microsoft Bot Framework team did a pretty good job in styling the webchat component which is looking pretty good out of the box: But companies often have the need to apply their CI/CD guidelines on whatever they use.

Problem statement Imagine this: You are working on a Bot Framework bot and want to implement human handoff to give users the option to have a conversation with another human being if the bot is not able to answer the user’s questions. Now there are many good samples out there for channels like Microsoft Teams for example which demonstrate how to bring in a human into the loop. But I was trying to find a way to tell the webchat control (via DirectLine) that a given point in the conversation it should do something (escalate to an agent).

As I teamed up with Albert-Jan Schot, Rick Van Rousselt & Thomas Gölles to create a project called SelectedTech, where we discuss and share our knowledge on Office 365/Teams/SharePoint & Bots and record that as webinars, we had the idea of doing something which would be a cool way of helping others with one of the Cognitive Service APIs - QnA Maker. The idea was to create a Postman collection which we’ll share later on, to bundle all API endpoints for the QnA Maker to easily test and debug the API.

As every year, Microsoft is hosting it’s Build conference again in Seattle aiming at showing the world all news around the latest developer tools and tech. As there is a ton of news, as usual, this blog post should give you an overview on the news and infos on the Conversational AI announcements at Build: Bot Framework Adaptive Dialog | docs | C# samples :: Simplify the development of sophisticated dialogs by dynamically updating as the conversation unfolds.

This post is something I wanted to do for a long time already. In my opinion, the topic of CI/CD can be very beneficial in terms of easier deployments and delivery of solutions. That is why I decided to run through the process of a creating a CI/CD pipeline for an Azure Bot Service based on Azure DevOps which I want to share with you with this post. Preparation First of all, we need to prepare a couple of things before we can actually start building our CI/CD pipeline:

I’m super excited to announce something I have been building for a couple of months now. I teamed up with Matt Wade and Niels Gregers Johansen who are the initiators of the Periodic Table of Office 365 to build the Periodic Table of Azure Cognitive Services. This table should help to understand which Cognitive Services are currently out there, including the following: Categorization Preview status indicators Links to the product pages As this is the first version for this kind of table, there will be more features in later releases, including description pages.

Developing bots can be a lot of work for those of you, who may not know which tools Microsoft offers for the various bot development stages. Fortunately, Microsoft released an article which contains the various tools for the following phases in your end-to-end bot development workflow. The following phases need to be gone through combined with the correct toolset supporting your successful bot development project: Plan Plan your bot’s conversations and dialogs Take a look at the principles of bot design Use Chatdown to mock conversations before you actually start coding Build Use Ludown to bootstrap Language Understanding Use MSBot to keep track of service references Use LUIS CLI to create and manage LUIS applications Use QnA Maker CLI to create QnA Maker knowledgebases Use Dispatch CLI to create your own dipsatch models Test Use the Bot Framework Emulator V4 for testing your bot Publish Use the Azure CLI to create, download and publish your locally built bots to the Azure Bot Service Connect Use the Azure CLI to connect your bot to the various Azure Bot Service channels Evaluate Use the Azure Portal to improve the bot’s capabilities and performance Use Application Insights to gain conversation-level reporting on user, message and channel data The full image can be downloaded here: End-to-End Bot Development Workflow

In the modern AI-enabled world, digital agents or bots (aka chatbots) are an extremely hot topic, which every company no matter of their industry will at least think about at the moment. Many big software vendors like Microsoft, Amazon, Google, you name it are offering their own set of tools and services which developers and architects can use in order to build such a conversational AI interface (aka chatbot). But in this modern world, it is always about being fast and have a short time to market, which often results in releases which are not quite ready.

Almost a year ago I published one of my first infographics which should visualize the various components of the Microsoft Bot Framework. Back then, the Bot Framework was available in version 3.0 and as you can imagine, since then there have been a lot of updates and changes. Some weeks ago, Microsoft announced that version 4.0 is now GA with many improvements and enhancements. Therefore, I decided to update my version of the infographic in order to visualize what changed and what’s important when talking about the Bot Framework, which I also renamed the “Microsoft Conversational AI Landscape”:

Every year somewhere at the end of September Microsoft’s biggest conference Ignite attracts thousands of people with a massive amount of news and big announcements. To keep up with all these awesome news is nearly impossible, as there is so much to explore in this very short period of time. Therefore, I try to sum up and link to all big announcements and news Microsoft made during the Ignite 2018 on the topic of conversational AI…

In the “jungle” of documentation and resources on the internet nowadays, it’s tough to keep the overview and know where to look for in order to seek new information about certain topics. Microsoft is now trying to unify the documentation across their products in order to have 1 place to go for. But until that is fully established, we need to conduct multiple resources. To overcome this issue in the field of the Azure Bot Service, I already released a resource cheat sheet in order to have all important sources in your pocket.

Nowadays, there is a ton of information out there for every service Microsoft has released, which is a good thing. But there are times when you search for something special, but you can’t find it as there are too many sites and resources which you need to crawl to find what you are looking for. The same is true for the Azure Bot Service and the MS Bot Framework, as there is plenty of documentation, but it is spread over a couple of different sites.

“Copy, paste… Bot!" - This is the slogan of the QnAMaker.ai service, which is one of the Cognitive Services in the knowledge category. This service offers a very easy-to-use toolset for creating, populating and publishing knowledgebases for FAQs which can be consumed by the Azure Bot Service very easy to build smart QnA Bots in a very short period of time. As the creation of such a QnAMaker knowledgebase is straightforward, the following infographic should help illustrate the process of creating, populating, testing and publishing a KB through the QnAMaker service.

A couple of months ago, I did a post about the Microsoft Cognitive Services with an infographic attached in order to better understand the various services and offerings in that area. Now a couple of months later, some things changed in that field, especially the offerings and the services (some disappeared, some have been merged and some appeared). Therefore, I updated my infographic in order to reflect the current services which are available:

A couple of weeks ago at the Microsoft Build conference (see this post for details on the announcements concerning AI), there were some cool new tools for the Bot Builder announced. These tools are already in preview and can be downloaded here. The following table should give an overview of the new tools released: Tool Description Chatdown Prototype mock conversations in markdown and convert the markdown to transcripts you can load and view in the new V4 Bot Framework Emulator MSBot Create and manage connected services in your bot configuration file LUDown Build LUIS language understanding models using markdown filese LUIS Build LUIS language understanding models using markdown files QnAMaker Create and manage QnAMaker.

Microsoft’s annual BUILD conference took again place from 7th - 9th May 2018 with a lot of new and awesome updates for the Azure AI platform. The following list should give you a short overview of all news and announcements: Bot Builder SDK v4 now in preview: Bot Builder SDK v4 .NET Bot Builder SDK v4 JavaScript Bot Builder SDK v4 Python Bot Builder SDK v4 Java Bot Framework Emulator v4 now in preview: Updates and Improvements “The new emulator includes features like an improved dynamic layout model, support for multiple bot configurations, simplified bot components management, and the ability to inspect responses from and deep link to connected services such as LUIS and QnA.

The idea of this solution has come to my mind a couple of months ago when doing a couple of conference sessions. During the sessions, I had some questions coming from the audience, which is a good thing for me, but somehow I noticed, that those a more or less general questions rather than deep technical ones. Therefore, I thought it would be helpful for me as well as for the audience to have a much simpler way of asking me questions during speaking engagements, but also whenever someone has a question which might be answered by me.

Microsoft has GAed the Azure Bot Service a couple of weeks ago, which was a huge step forward for all of us who are doing bots and AI apps in the Microsoft ecosystem. With this step, Microsoft is also pushing one more area into the Azure portal which is bots. Remember, during the preview, you had to create your bot in the Bot Framework portal and add your channels and everything related to your bot in that specific portal.

Have you ever faced the issue of looking at more than 200.000 lines of code after creating a freshFunctions Bot via theAzure Portal? Well, I have unfortunately and I didn’t know where to put my code in this massive amount of NodeJS code. As a lightweight developer, I am not that familiar in reading someone else’s code so I didn’t want to spend hours in studying the code below in order to get an idea where to put my code into.

The terms “Modern Workplace” or “Digital Workplace” is something every one of us have heard about in the last couple of months probably. It is some trend which should improve the workplace situation in order to digitally transform the way we work and make it more easier to communicate and collaborate with others. Therefore, the use of smart services can be beneficial when it comes to really transforming your workplace into a modern one.

With the announcement of the GA status of the Language Understanding Intelligent Service a couple of weeks ago, I was thinking about how to easily enable people in using this awesome Cognitive Service. I was asked many times on how to easily use that service but I faced a lot of open questions and confusion. That’s why I decided to create a cheat sheet to illustrate the process of creating, training and publishing a LUIS model in order to use it within a Bot or other apps which should act intelligent and understand the human language.

“Artificial intelligence productivity for every developer and every scenario” - this is the slogan which is shown on the landing page of the Microsoft AI Platform. But many people don’t know which services are included in this broad AI ecosystem. It is not only about Bots or Machine Learning - it’s about much more like Cognitive Services, it’s about the tools used for developing AI apps and the frameworks underneath all those great and intelligent services.

Yesterday I wanted to deploy a new bot with the Azure Bot Service to start developing a new digital assistant within the Azure Bot Framework platform. But as I created my bot from the Azure portal I ran into a strange error upon testing the bot immediately, which you can see below: As you can see the bot’s function started, but it didn’t finish successfully and in the test window I got the error message “couldn’t send retry” which was quite strange as I didn’t modify the code at all.

Microsoft’s Bot Framework is a great platform for designing, developing and deploying bots for various use cases. As nowadays many people are familiar with bots and they get used to connect and communicate with bots via social media, it’s a good opportunity to bring a service to the next level by adding a bot to it for a better communication service. In the past, t was very hard to achieve that as you would basically have to develop the bot on your own and connect all the different systems and platforms together to make the bot available to a broader audience.

One of the coolest Cognitive Services APIs is finally generally available: the Text Analytics API. If you have a look at **Survey 365 **you will see what awesome stuff the Text Analytics API lets you do. Like in this example, you can extract text and detect the sentiment score of a given text to see if it is rather positive or negative. And all that without coding a lot. You can see the official announcement from the Microsoft Azure team about the GA information below:

During the work on my first infographic on Cognitive Services I had a hard time to get the right icons for all Cognitive Services and APIs as I used Visio to create it and there were no stencils for these services. So I decided to go with the icons, Sandro Pereira created, which are totally awesome, but unfortunately, there are some service icons missing. So I had to create the missing icons on my own in Visio, which resulted in a huge amount of working hours to get this done.

As the Microsoft Ignite is currently going on in Orlando, there are a lot of news and updates on all the various areas. The main focus areas which have been presented in the keynote are: Modern workplace Business applications Applications & infrastructure Data & AI As you might already know I’m into all things AI, which is why I want to share the most recent and important updates on the AI ecosystem (Cognitive Services, Bots & ML) by Microsoft during the Ignite with you…

Microsoft Cognitive Services are APIs and services which can be used to add intelligent services to applications for different purposes, whether this is a web, mobile or desktop app or a bot which should interact with humans in a more natural way. But as there is a wide range of Cognitive Services which can be used this post should expose and describe each of the APIs currently available. This should help get to know Microsoft’s AI ecosystem a bit more.

Smart homes, smart vehicles, smart everything - nowadays all devices which we daily use have to be smart otherwise we wouldn’t use them, would we? There are so many different approaches to make a device smart either you can control it remotely, speak to it or it even sets actions autonomously which should ease our lifes here and there. The key to achieve all this is nearly the same everywhere, no matter if you use smart devices and services provided by Google, Amazon or Microsoft.

Microsoft Forms is one of the youngest services in the Office 365 ecosystem. Forms is a great service in my opinion as such a service was missing in the Microsoft repertoire. Now Office 365 users can create their Forms within seconds in order to create a survey or quiz and ask others about their opinion. And one of the most used survey topics in a company is the wellbeing of the employees.

Many of my customers, where I had the chance to do a workshop about Azure or Office 365 asked me the same question every single time: “How do you keep track with the updates on the roadmap?” I have to admit, the question is eligible due to the fact, that there is an update on the roadmap for both products nearly every day. So as a good consultant, you would need to spend all of your time studying the roadmaps to be on track and give your customers the newest information and advices in order to sell the best services and solutions.

The last days I was doing a lot of researches on AI and Cogntive Services as this topic is a major topic for the future in the daily use. So I read a couple of blog posts about this and got an idea on how to use it. And today I want to show you a small prototype application which I created in order to combine the Azure Cognitive Service API with my most used tool for work, which is Outlook and packed it into an Azure Logic App as I am not a developer (as you might know by now) in order to save development effort.

As you may know, Microsoft has released a Framework for building Bots on an Azure. You may encounter the problem that you are thrilled to start building a bot, but you don’t know how to begin and where to find the resources. So I thought of providing you with a list of useful resources and links on the Bot Framework which you may want to conduct: Links Official Microsoft Bot Framework Blog Bot Framework - Making Bots More Intelligent Forwarding activities / messages to other dialogs in Microsoft Bot Framework New Bot Framework features and updates Bots are banging: Getting Started with the Microsoft Bot Framework & Node.

Computer Vision Most of us will quickly bring image analysis in conjunction with high mathematical formulas and pattern recognition algorithms. The Computer Vision API but is based on a completely different level of abstraction. If one sends a picture of a squirrel on the service, “a squirrel is sitting on a rock” and a percentage chance comes in response, how much you can trust the statement. In addition, their position, as well as dominant color values can be queried tags and categories (animal, outdoor etc.

Microsoft just published a new article on the topic of Cognitive Services (formerlyProject Oxford). The Cognitive Services are a collection of SDKs and APIs along with services which should make your life easier if you want to build an intelligent application. These services basically leverage Microsoft’s existing platforms and services like Machine Learning and so on to enable developers to create applications with intelligent features like the following: Emotion Detection Video Detection Speech Detection Face Recognition Speech Recognition Vision Recognition Language Understanding Speech Understanding Our vision is for more personal computing experiences and enhanced productivity aided by systems that increasingly can see, hear, speak, understand and even begin to reason.