Install trusted skills once, then use them across agents and projects.

Two new features have just arrived in AI Assistant to address this issue: the skills manager and the skill repository. Together, they make skills easier to discover, trust, and reuse. Instead of keeping skills tied to one agent or project you can install them once and use them wherever they are needed while managing them from inside the IDE.

In practice, a skill gives an agent reusable capabilities for a specific task. The problem is that skills often stay tied to a single setup. They live with one agent, one repository, or one machine. Skills Manager changes that by making them reusable across projects and supported agents.For a quick introduction to what skills are, check out this great explanation from PyCharm Content Creator Kristel Cocoli.

Skills manager: One place to install, manage, and reuse skills

Skills are useful because they give agents reusable capabilities for specific developer tasks, whether that is debugging CI failures, working through PR comments, automating browser flows, or converting Java to Kotlin. The problem is that they often stay locked to a single setup.

Skills Manager fixes that by adding a new IDE layer for skill management. That means developers can install skills once inside the IDE and make them available across supported agents and across all projects opened in that IDE, instead of rebuilding the same setup over and over.

It also supports different ways of working depending on the task. Some skills belong at the IDE level, where they stay available across projects for an individual developer. Some belong at the project level, where they can travel with the repository and be shared through version control. Others are best kept agent-specific, tied to a dedicated workflow like CI triage, frontend work, or code review.

That is the core improvement: Skills Manager gives developers one place to discover recommended skills, choose the right scope for each one, and keep those skills available where the work actually happens.

Skill repository: A verified starting point

The skills manager makes skills easier to use. The skill repository makes them easier to get started with.

At launch, the repository gives you a JetBrains-filtered and verified list of skills, organized for easier discovery and reuse. Instead of building a collection from scratch or managing it by hand, you get a curated starting point with skills that are ready to be installed.

The repository is also designed to make adoption safer. New additions are screened to detect prompt injection, data exfiltration, and malicious code patterns. Attribution is preserved by using the skill’s own author metadata when available, or otherwise crediting the upstream maintainer or organization. That gives you a practical starting point you can trust, with useful skills, a safer adoption path, and clear credit to the people who created them.

Recommended skills to try first

Here are just a few examples from the repository that show the range of tasks where skills can be used to guide agents with improved accuracy. To view the full repository, check out this link: https://github.com/JetBrains/skills. 

• React-best-practices –  Reusable React and Next.js guidance for writing, reviewing, and refactoring frontend code.
• postgres-best-practices – Practical guidance for Postgres queries, schema design, performance, and security.
• playwright – A structured way to automate and debug real browser flows.
• pnpm – Better support for pnpm-based JavaScript projects, including workspaces and CI usage.
• kotlin-tooling-java-to-kotlin – Support for disciplined migration from Java to idiomatic Kotlin.

Transparency and limitations

Skills Manager introduces an IDE-wide layer so developers can install skills once and make them available across supported agents and all projects opened within the IDE. That is the recommended default experience, but support is not universal yet.

• Today, IDE-wide skill storage is supported in AI Assistant Chat for Codex and Claude Agent. Support for Junie and other ACP agents is coming.
• For CLI workflows, the experience is different. CLI agents cannot use IDE-installed skills, so terminal-based workflows still rely on project-level or agent-specific installation.
• Support also differs slightly between agents. For example, most agents can work with shared project-level skills, while Claude Agent uses its own agent-specific location  (.claude folder), instead of the shared project location.
• The repository will also keep expanding over time, including with more IDE-specific skills.

Get started

To get started, install the AI Assistant plugin in your JetBrains IDE. In the AI chat, click the + button and go to Skills to add, remove, and manage the skills that fit your workflow.

Ryan welcomes Harsha Chintalapani, co-founder and CTO at Collate and co-creator of Open Metadata, to the show to discuss why AI and LLMs struggle with real-time, structured production data.

Agentic AI is rapidly moving beyond demos and chatbots toward long‑running, autonomous systems that reason, call tools, collaborate with other agents, and operate reliably in production.

On April 3, 2026, Microsoft marked a major milestone with the General Availability (GA) release of Microsoft Agent Framework 1.0, a production‑ready, open‑source framework for building agents and multi‑agent workflows in.NET and Python. [techcommun...rosoft.com]

In this post, we’ll deep‑dive into:

• What Microsoft Agent Framework actually is
• Its core architecture and design principles
• What’s new in version 1.0
• How it differs from other agent frameworks
• When and how to use it—with real code examples

What Is Microsoft Agent Framework?

According to the official announcement, Microsoft Agent Framework is an open‑source SDK and runtime for building AI agents and multi‑agent workflows with strong enterprise foundations.

Agent Framework provides two primary capability categories:

1. Agents

Agents are long‑lived runtime components that:

• Use LLMs to interpret inputs
• Call tools and MCP servers
• Maintain session state
• Generate responses

They are not just prompt wrappers, but stateful execution units.

2. Workflows

Workflows are graph‑based orchestration engines that:

• Connect agents and functions
• Enforce execution order
• Support checkpointing and human‑in‑the‑loop scenarios

This leads to a clean separation of responsibilities:

This separation is a foundational design decision.

High‑Level Architecture

From the official overview, Agent Framework is composed of several core building blocks:

• Model clients (chat completions & responses)
• Agent sessions (state & conversation management)
• Context providers (memory and retrieval)
• Middleware pipeline (interception, filtering, telemetry)
• MCP clients (tool discovery and invocation)
• Workflow engine (graph‑based orchestration)

Conceptual Flow

 

 

 

 

 

 

 

 

🌟 What’s New in Version 1.0

Version 1.0 marks the transition from "Release Candidate" to "General Availability" (GA).

• Production-Ready Stability: Unlike the earlier experimental packages, 1.0 offers stable APIs, versioned releases, and a commitment to long-term support (LTS).
• A2A Protocol (Agent-to-Agent): A new structured messaging protocol that allows agents to communicate across different runtimes. For example, an agent built in Python can seamlessly coordinate with an agent running in a .NET environment.
• MCP (Model Context Protocol) Support: Full integration with the Model Context Protocol, enabling agents to dynamically discover and invoke external tools and data sources without manual integration code.
• Multi-Agent Orchestration Patterns: Stable implementations of complex patterns, including:
  • Sequential: Linear handoffs between specialized agents.
  • Group Chat: Collaborative reasoning where agents discuss and solve problems.
  • Magentic-One: A sophisticated pattern for task-oriented reasoning and planning.
• Middleware Pipeline: The new middleware architecture lets you inject logic into the agent's execution loop without modifying the core prompts. This is essential for Responsible AI (RAI), allowing you to add content safety filters, logging, and compliance checks globally.
•  DevUI Debugger: A browser-based local debugger that provides a real-time visual representation of agent message flows, tool calls, and state changes.

Code Examples

Creating a Simple Agent (C#)

From Microsoft Learn :

using Azure.AI.Projects; using Azure.Identity; using Microsoft.Agents.AI; AIAgent agent = new AIProjectClient( new Uri("https://your-foundry-service.services.ai.azure.com/api/projects/your-project"), new AzureCliCredential()) .AsAIAgent( model: "gpt-5.4-mini", instructions: "You are a friendly assistant. Keep your answers brief."); Console.WriteLine(await agent.RunAsync("What is the largest city in France?"));

This shows:

• Provider‑agnostic model access
• Session‑aware agent execution
• Minimal setup for production agents

Creating a Simple Agent (Python)

from agent_framework.foundry import FoundryChatClient from azure.identity import AzureCliCredential client = FoundryChatClient( project_endpoint="https://your-foundry-service.services.ai.azure.com/api/projects/your-project", model="gpt-5.4-mini", credential=AzureCliCredential(), ) agent = client.as_agent( name="HelloAgent", instructions="You are a friendly assistant. Keep your answers brief.", ) result = await agent.run("What is the largest city in France?") print(result)

The same agent abstraction applies across languages.

When to Use Agents vs Workflows

Microsoft provides clear guidance:

Key principle:
If you can solve the task with deterministic code, do that instead of using an AI agent.

🔄 How It Differs from Other Frameworks

Microsoft Agent Framework 1.0 distinguishes itself by focusing on "Enterprise Readiness" and "Interoperability."

🛠️ Key Technical Components

1. Agent Harness: The execution layer that provides agents with controlled access to the shell, file system, and messaging loops.
2. Agent Skills: A portable, file-based or code-defined format for packaging domain expertise.

Implementation Tip: If you are coming from Semantic Kernel, Microsoft provides migration assistants that analyze your existing code and generate step-by-step plans to upgrade to the new Agent Framework 1.0 standards. Microsoft Agent Framework Version 1.0 | Microsoft Agent Framework

Agent Framework documentation

🎯 Summary

Microsoft Agent Framework 1.0 is the "grown-up" version of AI orchestration. By standardizing the way agents talk to each other (A2A), discover tools (MCP), and process information (Middleware), Microsoft has provided a clear path for taking AI experiments into production.

For more detailed guides, check out the official Microsoft Agent Framework DocumentationMicrosoft Agent Framework - .NET AI Community Standup

 

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Imagine this sweat-inducing nightmare scenario. A banking customer's phone is stolen, and your mobile app is logged in, granting the thief complete access to their account. A frantic call comes into support. Every second counts. What is your speed-to-response for revoking that active session and securing their funds?

If you're relying on standard self-contained JWTs, the honest answer might be "up to an hour", depending on how long the token is valid. That's not going to cut it. Let's talk about how Reference Tokens give you an emergency stop button for exactly these situations, and how to wire it all up with Duende IdentityServer in .NET 10.