# Agent Framework Published articles for Agent Framework. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Build Your Own AI Agent Harness in C#, the MafClaw Live Series DevFeed: [Build Your Own AI Agent Harness in C#, the MafClaw Live Series]() Original publisher: [Read original article]() Author: Bruno Capuano Published: 2026-09-16T21:00:00Z Content type: tutorial Language: en Sources: [.NET Blog]() Topics: [Agent Harness](), [Microsoft Agent Framework](), [AI Agent](), [C#](), [.NET]() Tags: [agent](), [agent-framework](), [agent-harness](), [ai](), [building](), [c-sharp](), [cloud](), [csharp](), [microsoft-agent-framework](), [microsoft-reactor](), [net](), [observability]() ### AI overview This article introduces a four-part live series that builds a complete C# AI agent using the Microsoft Agent Framework harness. It describes adding tools, planning, file access, approvals, memory, skills, shell commands, code execution, background agents, observability, governance, evaluations, and hosted deployment. ### Source excerpt I am building a complete C# agent live, from a single call around an IChatClient to a production-ready, observable, governed agent, using the Microsoft Agent Framework harness in a 4-part Microsoft Reactor series. The post Build Your Own AI Agent Harness in C#, the MafClaw Live Series appeared first on .NET Blog. ## Build an interview coach app with the GitHub Copilot SDK DevFeed: [Build an interview coach app with the GitHub Copilot SDK]() Original publisher: [Read original article]() Author: Justin Yoo Published: 2026-09-15T16:00:11Z Content type: tutorial Language: en Sources: [Developer Blogs]() Topics: [GitHub Copilot](), [SDK](), [Model Context Protocol](), [.NET](), [Blazor]() Tags: [agent-framework](), [ai](), [blazor](), [github-copilot](), [github-copilot-sdk](), [mcp](), [microsoft-agent-framework](), [microsoft-foundry](), [model-context-protocol](), [net](), [sdk]() ### AI overview This tutorial demonstrates how to build an interview coach application with the GitHub Copilot SDK in .NET. The sample uses a Blazor chat interface, Copilot instructions and callable tools, and Model Context Protocol services for document handling and interview records. It emphasizes giving Copilot application-specific tools rather than access to shell commands or source files. ### Source excerpt An interview coach has to do more than ask questions. It needs to read a resume, follow up on an incomplete answer, and save enough context to give useful feedback at the end. Some of that work is conversation. Some of it requires calling an application service. The GitHub Copilot SDK lets you use the [...] The post Build an interview coach app with the GitHub Copilot SDK appeared first on Microsoft for Developers. ## Temporal expands its Google Cloud Partnership with Gemini integration and pay-as-you-go pricing on Google Cloud Marketplace DevFeed: [Temporal expands its Google Cloud Partnership with Gemini integration and pay-as-you-go pricing on Google Cloud Marketplace]() Original publisher: [Read original article]() Author: Jay Sivachelvan Published: 2026-08-31T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Google Cloud Platform (GCP)](), [SDKs](), [Google](), [reliability](), [Artificial Intelligence](), [Cloud Run](), [Resilience](), [API](), [Serverless]() Tags: [agent](), [agent-development-kit](), [agent-framework](), [announcements](), [api](), [billing](), [cloud-marketplace](), [cloud-run](), [execution](), [gemini](), [google](), [google-cloud](), [integration](), [launch](), [partnership](), [pricing]() ### AI overview Temporal announces expanded Google Cloud integrations, including Public Preview support for the Google Gen AI Python SDK, durable execution for Gemini-based workflows, and pre-release Serverless Workers for Google Cloud Run. Temporal Cloud is also available on Google Cloud Marketplace with pay-as-you-go pricing. ### Source excerpt Temporal expands its Google Cloud partnership with Gemini integration, durable execution, and pay-as-you-go pricing on Google Cloud Marketplace. ## How Temporal Adds Durability to AWS Agentic AI Workloads DevFeed: [How Temporal Adds Durability to AWS Agentic AI Workloads]() Original publisher: [Read original article]() Author: Rahman Syed Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Amazon Web Services](), [Artificial Intelligence](), [Agent Framework](), [Amazon Bedrock](), [Strands Agents](), [AWS Lambda]() Tags: [agent-framework](), [agentic](), [ai](), [amazon-bedrock](), [aws](), [aws-lambda](), [cloud](), [iam](), [temporal-voices]() ### AI overview This developer article explains how Temporal adds a durability layer to AWS-based agentic AI systems. It presents a loan-underwriting example and describes using Amazon Bedrock, Strands Agents, and AWS Lambda to build recoverable, scalable workloads. ### Source excerpt AWS gives developers the best-in-class tools for building agentic AI -- models, an agent framework, and compute to run it all on. As those systems grow more complex with more tools, more agents, and longer-running steps that wait on human-intervention, keeping the whole thing consistent and recoverable becomes its own design challenge. This is the layer Temporal adds. ## LangGraph in production: Temporal's LangGraph Plugin adds Durable Execution DevFeed: [LangGraph in production: Temporal's LangGraph Plugin adds Durable Execution]() Original publisher: [Read original article]() Author: Brian Strauch Published: 2026-07-16T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Langgraph](), [Orchestration](), [Agent Framework](), [human review](), [Python](), [telemetry]() Tags: [agent-framework](), [crash-recovery](), [human-review](), [langgraph](), [orchestration](), [product-news](), [python]() ### AI overview Temporal announces a public-preview LangGraph integration for Python, allowing LangGraph agents to run on Temporal with automatic failure recovery, durable execution, human-in-the-loop waits, and support for long-running work. The announcement also covers LangSmith integrations for Python and TypeScript. ### Source excerpt LangGraph agents now run on Temporal: automatic crash recovery, free human-in-the-loop waits, and full observability via new LangSmith integration. ## A2A in Java: hands on Embabel DevFeed: [A2A in Java: hands on Embabel]() Original publisher: [Read original article]() Author: Aneshka Goyal Published: 2026-06-16T08:44:44Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [Embabel](), [A2A protocol](), [Java](), [Spring AI](), [Agent Framework](), [interoperability](), [Artificial Intelligence](), [Large Language Model]() Tags: [a2a](), [a2a-protocol](), [agent-framework](), [ai](), [embabel](), [interoperability](), [java](), [large-language-models-llms](), [software-engineering](), [spring-ai]() ### AI overview This hands-on article introduces A2A in Java and explores Embabel, a Java agent framework built on Spring AI. It explains how A2A enables interoperability among agents across languages, frameworks, vendors, and platforms, and describes Embabel's use of goal-oriented action planning with existing Java domain logic and structured code. ### Source excerpt Part 1: What is Embabel? A2A, or Agent-to-Agent Protocol, is an open standard (now under the Linux Foundation) that provides a common language for diverse AI agents to communicate, collaborate, and share tasks, enabling interoperability across different frameworks, vendors, and platforms. You can read more about A2A here. A2A makes agents framework and tech stack agnostic, which means agents developed in Python will be able to connect to other agents in Java or any other programming language of choice (given we have A2A protocol compliance). We currently have ADK (Python) and other Python specific frameworks that simplify the development of A2A compliant agents (as you'll read in this blog). This initiative explores creating a comparable Java-specific framework to enable building A2A-compliant agentic applications in Java. Brief about Spring AI Before learning about Embabel, it's important to discuss and know about Spring AI which provides the foundation for Embabel framework. The Spring AI project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity. This project draws inspiration from notable Python projects, such as LangChain and LlamaIndex, but Spring AI isn't a direct port of those projects. The project was founded with the belief that the next wave of generative AI applications will not only be for Python developers but ubiquitous across many programming languages. Spring AI can be used without Embabel (or any other high level framework like ADK java) to create agents and agentic applications. For more info around how to use Spring AI, check out Part 1 and Part 2. What is Embabel? Embabel is an agent framework built on Spring AI, designed to integrate Large Language Models (LLMs) with existing domain logic and structured code in Java applications. It utilizes a Goal-Oriented Action Planning (GOAP) mechanism to allow agents to dynamically determine the sequence of actions needed ## Inside the Google ADK and Temporal integration DevFeed: [Inside the Google ADK and Temporal integration]() Original publisher: [Read original article]() Author: Drew Hoskins Published: 2026-04-20T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Agent Framework](), [Artificial Intelligence](), [Google](), [Development](), [Python](), [Remote Procedure Call (RPC)](), [JSON](), [vercel ai sdk](), [OpenAI]() Tags: [agent-development-kit](), [agent-framework](), [agents](), [ai](), [announcements](), [distributed](), [google-adk](), [http](), [integration](), [json-rpc](), [python]() ### AI overview This article explains Temporal's integration with Google ADK. It describes Temporal as the durable execution layer while Google ADK provides the agent framework, and outlines an initial Python implementation using independent HTTP services and A2A JSON-RPC requests. The article also places the integration alongside Temporal's OpenAI Agents SDK and Vercel AI SDK integrations. ### Source excerpt Google ADK builds the agent and Temporal makes sure it finishes. Here's how our integration works under the hood and all it took to make it happen. ## Using agents as scaffolding for recurring security-vulnerability tasks DevFeed: [Using agents as scaffolding for recurring security-vulnerability tasks]() Original publisher: [Read original article]() Published: 2026-04-12T17:00:00Z Content type: opinion Language: en Sources: [Will Larson - Irrational Exuberance]() Topics: [Agent Framework](), [Vulnerabilities](), [GitHub](), [Dependabot](), [Model Context Protocol](), [Slack]() Tags: [agent-framework](), [agents](), [dependabot](), [github](), [mcp](), [security-vulnerabilities](), [slack]() ### AI overview The article examines using agents to reduce human involvement in recurring software-company processes. It describes an internal workflow that receives GitHub Dependabot webhooks, filters vulnerability alerts, identifies issue owners through GitHub MCP, and sends alerts to Slack, while noting that the agent could not reliably limit notifications to critical-severity alerts. ### Source excerpt One of my gifts/curses is an endless fixation with how processes can be optimized. For a brief moment early in my career, that was focused on improving how humans collaborate, but that quickly switched to figuring out how we can minimize human involvement, and eliminate human-to-human handoffs as much as possible. Lately, every time I perform a recurring task-or see someone else perform one-I think about how we might eliminate the human's involvement entirely by introducing agents. This both has worked well, but also worked poorly, and I wanted to highlight the pattern I've found useful. For a concrete example, a problem that all software companies have is patching security vulnerabilities. We have that problem too, and I check our security dashboards periodically to ensure nothing has gone awry. Sometimes when I check that dashboard, I'll notice a finding that's precariously close to our resolution SLAs, and either fix it myself or track down the appropriate team to fix it. However, this feels like a process that shouldn't require me checking on it. Five to six months ago, I added Github Dependabot webhooks as an input into our internal agent framework. Then I set up an agent to handle those webhooks, including filtering incoming messages down to the highest priority issues. About a month ago, when I upgraded from GPT 4.1 to GPT 5.4 with high reasoning, I noticed that it got quite good at using the Github MCP to determine the appropriate owners for a given issue, using the same variety of techniques that a human would use: looking at Codeowners files where available, looking at recent commits on the repository, and so on. The alerts and owners were already getting piped into a Slack channel. So, this worked! However, it didn't actually work that well, because despite repeated iteration on the prompt, including numerous CRITICAL: you must... statements, it simply could not reliably restrict itself to critical severity alerts. It would also include some high severity ## Watch the recordings from my Python + MCP series DevFeed: [Watch the recordings from my Python + MCP series]() Original publisher: [Read original article]() Author: Pamela Fox (noreply@blogger.com) Published: 2025-12-19T15:50:00Z Content type: tutorial Language: en Sources: [Pamela Fox]() Topics: [Model Context Protocol](), [MCP Server](), [Python](), [Azure](), [OAuth](), [Docker](), [OpenTelemetry](), [LangChain](), [GitHub Copilot](), [vs-code](), [Microsoft Agent Framework]() Tags: [agent-framework](), [azure](), [code](), [deployment](), [docker](), [github-copilot](), [langchain](), [mcp](), [mcp-server](), [microsoft-agent-framework](), [model-context-protocol](), [oauth](), [opentelemetry](), [python](), [vs-code]() ### AI overview A three-part Python and MCP series provides recordings, slides, and open-source code covering MCP server development with FastMCP, cloud deployment on Azure, observability, networking, and authentication. ### Source excerpt MCP is one of the fastest growing technologies in the Generative AI space this year, and the first AI related standard that the industry has really embraced wholeheartedly. I just gave a three-part live stream series all about Python + MCP. I showed how to: Build MCP servers in Python using FastMCP Deploy them into production on Azure (Container Apps and Functions) Add authentication, using either Keycloak and Microsoft Entra as the OAuth provider All of the materials from our series are available and linked below: Video recordings of each stream Powerpoint slides Open-source code samples complete with Azure infrastructure and 1-command deployment If you're an instructor, feel free to use the slides and code examples in your own classes. Spanish speaker? My colleague delivered a fantastic Spanish version of the series. Building MCP servers with FastMCP 📺 Watch YouTube recording In the intro session of our Python + MCP series, we dive into MCP (Model Context Protocol). This open protocol makes it easy to extend AI agents and chatbots with custom functionality, making them more powerful and flexible. We demonstrate how to use the Python FastMCP SDK to build an MCP server running locally. Then we consume that server from chatbots like GitHub Copilot in VS Code, using it's tools, resources, and prompts. Finally, we discover how easy it is to connect AI agent frameworks like Langchain and Microsoft agent-framework to the MCP server. Slides for this session Code repository with examples: python-mcp-demos Deploying MCP servers to the cloud 📺 Watch YouTube recording In our second session of the Python + MCP series, we deploy MCP servers to the cloud! We walk through the process of containerizing a FastMCP server with Docker and deploying to Azure Container Apps. Then we instrument the MCP server with OpenTelemetry and observe the tool calls using Azure Application Insights and Logfire. Finally, we explore private networking options for MCP servers, using virtual networks th