How are AI agents changing the way enterprises automate business processes?
AI agents are becoming more capable of understanding requests, making decisions, and interacting with multiple systems through APIs and automation tools. I’m interested in how organizations are approaching this shift from traditional workflow automation toward more intelligent, agent-driven workflows. Some areas I’m exploring: How are teams deciding which processes are suitable for AI agents? What role do validation, monitoring, and human approval play in agent-based automation? How are organizations managing security and permissions when AI agents access business systems? Are AI agents replacing traditional workflows, or working alongside existing automation platforms? Would love to hear how the community is approaching AI agent adoption in real enterprise scenarios. What patterns or lessons have you learned while implementing AI-powered workflows?Design Patterns for Building Reliable AI Agent Workflows in Enterprise Applications
Hi Microsoft community, AI agents are becoming increasingly common in enterprise applications, but designing reliable workflows around them introduces new challenges. A common architecture pattern is separating the AI reasoning layer from the execution layer, where agents can make decisions while business logic, permissions, and validations remain within controlled services. I would like to understand how teams are approaching: - Designing scalable AI agent architectures - Connecting agents with enterprise APIs and services - Managing agent state and workflow execution - Handling failures, retries, and human approval steps - Monitoring and evaluating agent performance in production What architecture patterns and Microsoft technologies are you finding effective when building production-ready AI agent solutions? Looking forward to learning from the community.
Windows AI Foundry (AI Examples) - Access Denied
Hi ! I am trying to play and run the Windows AI Foundry Apps - but keep getting an error message: Access denied Sample code: if (LanguageModel.GetReadyState() == AIFeatureReadyState.NotReady) { var languageModelDeploymentOperation = LanguageModel.EnsureReadyAsync(); await languageModelDeploymentOperation; } LanguageModel _session = await LanguageModel.CreateAsync();Machine Learning Community Standup | August 17, 2022
Topic: Introducing SynapseML Recording Community Links .NET Data Hub Machine Learning with ML.NET for Absolute Beginners Plotly C# Bindings NER Announcing SynapseML for .NET Synapse ML Synapse ML GitHub Repo Paper: Large-Scale Intelligent Microservices Mosaic: find artistic connections with deep learning Resources ML.NET Website ML.NET Documentation Machine Learning Notebooks Machine Learning Samples Feedback We want to hear from you. Do you have topics you'd like to hear more about or have ideas on how we can improve the show? Take a few minutes to fill out our feedback form. Stay Connected Join the Virtual ML.NET Community Discord and the #machine-learning channel on the .NET Development Discord. Find recordings from previous shows on .NET Live TVWelcome to the Machine Learning and AI .NET space!
Hello and welcome to the Machine Learning & AI space! Here are some resources you might find useful: ML.NET Documentation: https://docs.microsoft.com/dotnet/machine-learning Samples: dotnet/machinelearning-samples: Samples for ML.NET, an open source and cross-platform machine learning framework for .NET. (github.com) Repository: dotnet/machinelearning: ML.NET is an open source and cross-platform machine learning framework for .NET. (github.com) Model Builder Repository: dotnet/machinelearning-modelbuilder: Simple UI tool to build custom machine learning models. (github.com) Machine Learning Community Standup (filter for Machine Learning in dropdown): .NET Community Standups | .NET Live TV (microsoft.com) .NET for Apache Spark Documentation: http://docs.microsoft.com/dotnet/spark Repository: dotnet/spark: .NET for Apache® Spark™ makes Apache Spark™ easily accessible to .NET developers. (github.com) Tools .NET Interactive Notebooks: dotnet/interactive: .NET Interactive takes the power of .NET and embeds it into your interactive experiences. Share code, explore data, write, and learn across your apps in ways you couldn't before. (github.com) .NET Interactive Notebooks VS Code extension: .NET Interactive Notebooks - Visual Studio Marketplace Visual Studio Notebook Editor extension: Notebook Editor - Visual Studio Marketplace Numerical & Statistical Libraries Math.NET Numerics: Math.NET Numerics (mathdotnet.com) FSharp.Stats: FSharp.Stats (fslab.org) Plotting / Graphic Libraries Plotly.NET: Plotly.NET Deep Learning Libraries TensorFlow.NET: SciSharp/TensorFlow.NET: .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#. (github.com) TensorFlow.Keras: NuGet Gallery | TensorFlow.Keras 0.6.4 TorchSharp: dotnet/TorchSharp: .NET bindings for the Pytorch engine (github.com) DiffSharp: DiffSharp: Differentiable Tensor Programming Made Simple OnnxRuntime: microsoft/onnxruntime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator (github.com)
