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Amazon Music Agent Feed - Simply Explained
Quick Answer: Agent Feed is an important Microsoft topic for teams that need clearer technical decisions, safer implementation, and practical operational value. This episode explains what it does, where it fits in the Microsoft ecosystem, and the key considerations for administrators, architects, and business stakeholders.
Agent Feed serves as a vital tool for managing AI agents within Power Apps. It enhances your ability to oversee AI tasks, ensuring effective collaboration between human users and technology. By integrating features like real-time activity logs and assistance requests, Agent Feed empowers you to maintain control over automated processes. This innovative approach significantly improves efficiency, reducing energy consumption and costs compared to traditional methods of AI management. With Agent Feed, you can confidently navigate the evolving landscape of AI, enhancing both productivity and oversight.
Key Takeaways
- Agent Feed centralizes AI management, allowing you to oversee tasks and ensure alignment with business goals.
- Real-time activity logs provide insights into agent performance, helping you make informed decisions quickly.
- The 'Request Assistance' feature boosts task success rates by enabling collaboration between you and AI agents.
- Task categorization simplifies management, making it easy to identify tasks that need attention or are completed.
- Implementing Agent Feed can lead to a 40% faster resolution rate and a 25% increase in customer satisfaction.
- Balancing automation with human oversight is crucial for effective AI management and decision-making.
- Adopt best practices like defining success metrics and assembling cross-functional teams to enhance implementation.
- Embrace the future of AI with Agent Feed to unlock new possibilities and improve operational efficiency.
Agent Feed Overview
Key Features of Agent Feed
Agent Feed is a powerful tool designed to enhance your experience with AI in Power Apps. It serves as a centralized hub where you can manage and supervise AI agents effectively. This tool is crucial for ensuring that AI operates within the guidelines you set, allowing for better control and oversight.
The significance of Agent Feed in AI management cannot be overstated. It empowers you to maintain a balance between automation and human intervention. With Agent Feed, you can oversee AI tasks, ensuring they align with your business objectives. This oversight is essential, especially as AI agents operate independently, making it challenging to trace actions back to specific user commands.
Here are the main functionalities of Agent Feed:
| Functionality | Description |
|---|---|
| Human-Agent Collaboration | Enables you to review, validate, and complete agent-generated tasks. |
| Unified Task Feed | Allows you to supervise and interact with agent work through a task-based interface. |
| Task Categorization | Organizes tasks into sections like 'Needs Attention' and 'Completed' for better management. |
You will find that tasks include essential details such as titles, descriptions, agent names, and timestamps. The 'Needs Attention' section highlights tasks requiring your input, while the 'Completed' section displays tasks finished by you or the agents.
One of the standout features of Agent Feed is the Log for Review. This feature supports compliance and auditing requirements by providing comprehensive audit logs. These logs are crucial for organizations that need to answer critical questions about agent actions. They ensure accountability by recording who performed actions, how, and when. This level of detail is vital for meeting regulatory requirements across various industries.
Another key feature is the Request Assistance function. This functionality enhances collaboration between you and AI agents. Studies show that using AI guidance significantly improves task success rates. For instance, when participants received AI assistance, their macro success rate reached 70%, compared to only 20% for unassisted guidance. This demonstrates how AI can serve as an effective training tool, helping you perform better in subsequent tasks.
Lastly, the Invoke Data Entry feature streamlines data management processes. It allows you to supervise, trust, and collaborate with your agents in a familiar task feed format. This integration simplifies your workflow, making it easier to manage data entry tasks efficiently.
How to Supervise Autonomous Agents
Real-Time Insights in Power Apps
Monitoring AI agents through Agent Feed is straightforward. Follow these steps to set up your environment for effective supervision:
- Sign in to Power Apps and select your contact center environment.
- In the site map, select Apps, search for the Copilot Service workspace, and edit it.
- Enable Agent Feed by:
- Selecting the Agents icon in the site map.
- Choosing the agent that corresponds to Case Management Agent, such as Case Processing Agent, and selecting the ellipses (...) > Add to app.
- Saving and publishing the changes.
- Add the Agent Supervisor view by:
- Searching for and selecting Case view.
- In the Cases pane, selecting Views.
- Choosing the ellipses (...) for Agent Supervisor and then selecting Add.
- Saving and publishing the changes.
- Finally, sign in to the Copilot Service workspace with a supervisor role to verify that the agent feed appears in the site map.
Human intervention plays a crucial role in AI decision-making. While AI agents can handle repetitive tasks, they may not always make the best choices in complex situations. Your expertise ensures that decisions align with business goals. By supervising autonomous agents, you maintain control over agent activity, allowing for timely adjustments when necessary.
Agent Feed provides real-time visibility into agent activities. You can track various metrics, including:
- Latency
- Throughput
- Success/error counts
- Resource utilization
These metrics help you understand how well your agents perform. Additionally, Agent Feed captures the execution flow of each request, giving you insight into every step of the process. This level of detail allows you to identify bottlenecks and optimize performance.
The integration with the Model Context Protocol (MCP) enhances the functionality of Agent Feed. MCP standardizes communication between AI applications and Power Apps. This integration simplifies the development process, reducing time and complexity for developers. It also allows AI applications to access a wide range of data sources, making them more capable. As a result, you benefit from more efficient AI applications that can act effectively on your behalf.
Practical Applications of Agent Feed
Enhancing Productivity with AI
Businesses can leverage Agent Feed in various ways to boost productivity. Here are some common scenarios where Agent Feed shines:
| Business Scenario | Description |
|---|---|
| Customer Support | Enhances workflows for customer support flows, providing better visibility and efficiency. |
| Sales | Assists sales agents in performing lookups, validations, and payment workflows. |
| Data Source Automation | Facilitates automation involving multiple data sources or connectors with step-by-step visibility. |
Integrating Agent Feed into your daily workflows offers numerous benefits. You can streamline processes and improve efficiency. For instance, businesses report a 40% faster resolution rate and a 25% increase in customer satisfaction after implementing Agent Feed. These improvements stem from the tool's ability to create actionable tasks and provide real-time insights into agent performance.
Agent Feed allows you to focus on complex tasks by automating routine processes. Here are some ways it achieves this:
- AI agents automate complex, multi-step work that requires reasoning and dynamic decision-making.
- They reduce manual effort by eliminating repetitive cognitive tasks such as reviewing documents and synthesizing data.
- AI agents improve consistency by applying the same reasoning and decision criteria across all cases.
- They accelerate processes, completing workflows in seconds or minutes instead of hours or days.
- AI agents scale expertise by codifying decision-making logic and best practices for consistent application.
For example, scheduling agents manage meetings by checking availability and sending invites, reducing time spent on emails. Data entry agents handle repetitive tasks like transferring information and validating data quality. Research agents gather and synthesize information from various sources, generating reports efficiently.
Balancing automation and human oversight is crucial. While AI can handle many tasks, your expertise ensures that decisions align with business rules. To effectively balance these elements, follow these steps:
| Step | Description |
|---|---|
| 1. Define evaluation objectives | Align business goals with evaluation criteria, such as customer satisfaction and regulatory compliance. |
| 2. Create test datasets | Develop realistic scenarios that represent real-world usage patterns, including edge cases. |
| 3. Select evaluation methods | Combine expert evaluation with automated metrics for comprehensive assessment. |
| 4. Implement measurement framework | Use real-time monitoring for customer-facing agents or batch assessment for internal tools. |
| 5. Analyze results and iterate | Establish feedback loops to connect evaluation results to model improvements. |
By integrating Agent Feed into your operations, you can ensure seamless collaboration between AI and human agents. This approach not only enhances productivity but also maintains the necessary oversight to meet your organization's goals.
Challenges in Implementing Agent Feed
Implementing Agent Feed can present several challenges for businesses. Understanding these obstacles helps you prepare for a smoother integration process. Here are some common issues you might encounter:
- Cost Observability: Tracking costs associated with Agent Feed is not straightforward. You need to implement a governance layer to monitor expenses effectively.
- Complexity with Cross-Environment Agents: Managing agents that operate across different environments can be tricky. You must handle authentication, auditing, and error management carefully.
- Data Privacy and Security Concerns: AI agents can introduce new vulnerabilities. They may become attractive targets for cyberattacks, which can compromise data privacy and security.
| Evidence | Explanation |
|---|---|
| AI agents introduce new vulnerabilities and risks | This highlights how the deployment of AI agents can lead to increased attack surfaces, impacting data privacy and security. |
| AI agents are attractive targets for cyberattacks | The mention of AI agents being targets emphasizes the security concerns that arise from their deployment. |
| Poor management can lead to financial losses and reputational damage | This illustrates the potential consequences of not addressing data privacy and security concerns. |
To overcome these challenges, you can adopt several best practices:
- Define Success: Establish 1-2 key performance indicators (KPIs) that align with your business outcomes. This will guide your development efforts and keep you focused on what matters.
- Assemble a Cross-Functional Team: Involve diverse stakeholders in the implementation process. This ensures that the agent adds value and meets various needs across your organization.
- Launch with Support: Provide training and clear communication to empower users. Gathering feedback during this phase can help you make necessary adjustments.
- Monitor and Adjust: Use real-time data to track performance. This allows you to make continuous improvements based on actual usage and outcomes.
Additionally, effective change management strategies can significantly enhance the adoption of Agent Feed. Organizations that implement structured change management for AI initiatives are 3.4 times more likely to achieve successful adoption. Focus on the human aspects of adoption, such as behavior change and role clarity.
Start by having leaders use AI personally to model behavior. This helps assess the current state of adoption accurately. Ensure that skills extraction occurs before workflow automation. This way, you codify the right knowledge before integrating it into AI-assisted processes. Lastly, encourage social learning with immediate application. This approach is more effective than generic training, as it promotes collaboration and practical use of AI tools.
By addressing these challenges and following best practices, you can successfully implement Agent Feed and enhance your organization's AI capabilities.
Agent Feed plays a crucial role in enhancing AI management and ensuring effective human oversight. By treating AI agents like new hires, you establish clear roles and expectations, which fosters accountability. This structured governance allows you to trust AI agents as active participants in your workflows.
With real-time activity monitoring, Agent Feed improves collaboration and decision-making, making it essential for optimizing operations. As you explore the potential of Agent Feed in your organization, consider how it can transform your approach to AI integration.
| Future Trend | Description |
|---|---|
| Automation | AI agents will automate repetitive tasks in Power Apps. |
| Enhanced User Experience | Improved governance will lead to better user interactions. |
| Supervised Actions | Human review in automated processes ensures quality. |
Embrace the future of AI with Agent Feed and unlock new possibilities for your business.
FAQ
What is Agent Feed?
Agent Feed is a supervision hub in Power Apps that allows you to manage AI agents effectively. It enhances human oversight and collaboration with AI, ensuring tasks align with your business objectives.
How does Agent Feed improve AI management?
Agent Feed provides real-time insights into AI agent activities. You can monitor performance metrics, review tasks, and intervene when necessary, ensuring that AI operates within your guidelines.
Can I customize the features of Agent Feed?
Yes, you can customize Agent Feed to fit your specific needs. You can adjust task categories, set evaluation criteria, and define key performance indicators to align with your business goals.
What types of tasks can AI agents handle?
AI agents can automate repetitive tasks, such as data entry and document review. They can also assist in complex workflows, allowing you to focus on higher-level decision-making.
How does Agent Feed integrate with model-driven apps?
Agent Feed seamlessly integrates with model-driven apps in Power Apps. This integration allows you to supervise AI agents directly within the applications you use daily, enhancing productivity and oversight.
Is training required to use Agent Feed?
While no extensive training is necessary, familiarizing yourself with Agent Feed's features will help you maximize its benefits. Microsoft provides resources and support to assist you in getting started.
What is the role of generative AI in Agent Feed?
Generative AI enhances Agent Feed by enabling AI agents to learn from interactions and improve their performance over time. This capability allows for more efficient task handling and better decision-making.
How can I ensure data privacy with AI agents?
To ensure data privacy, implement robust security measures and governance protocols. Regularly audit AI agent activities and maintain compliance with industry regulations to protect sensitive information.
🎧 You Should Also Listen To
- AI Agents – Simply Explained provides the next practical learning step and adds useful context for this topic.
- Microsoft Agent Framework – Simply Explained provides the next practical learning step and adds useful context for this topic.
- Microsoft Fabric Real-Time Intelligence – Simply Explained provides the next practical learning step and adds useful context for this topic.
Last reviewed: July 2026.
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Today's topic is one that almost everyone has heard of, but almost no one can actually explain.
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I'm talking about AI agents.
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You've probably heard the term in a meeting, read it in a blog post or seen it in a headline.
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Everyone's talking about them.
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But ask someone to define one, and the answers get fuzzy real fast.
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The hype is loud, the definitions are vague, and the phrase gets thrown around so loosely,
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it's lost almost all its meaning.
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Here's the most common mistake people make.
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They think an agent is just a chatbot with a fancier name.
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You ask it something, it replies, that's not an agent, that's a chatbot.
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And there is a real difference between the two.
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So what changed?
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Why is everyone suddenly obsessed with agents?
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And more importantly, why should you care if you're not a developer?
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By the end of this episode, you'll understand what an AI agent actually is,
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why Microsoft built a whole framework around them, and why this matters for the way you work.
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What exactly is an AI agent?
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You already know how a chatbot works, you type something, it responds.
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That's a conversation, it's reactive, it waits for you to say something, then it replies.
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An agent is different, it doesn't just answer.
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It acts, think, decide, act, that's the simplest definition I can give you.
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Here's how it works.
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First, the agent thinks.
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It uses a large language model to reason about what you actually need,
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not just what you typed, but what you meant.
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If you say book a meeting with Sarah next Tuesday,
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the agent doesn't just repeat that back to you,
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it thinks about what that request really means, then it decides.
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It breaks your request into steps.
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Book a meeting, becomes a plan, check your calendar, find a free slot,
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check Sarah's availability, send an invite.
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The agent doesn't guess, it creates a sequence of actions.
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Finally, it acts, it calls tools and services to make it happen.
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It doesn't tell you how to book a meeting, it books the meeting.
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It doesn't explain how to check the weather, it checks the weather and gives you the answer.
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So think about the difference, a chatbot is reactive.
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It waits for your next question.
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An agent is proactive, you give it a goal and it goes off and works on it.
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It might come back with questions if it needs more information,
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but it doesn't need you to hold its hand through every step.
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A chatbot is like a reference desk at a library.
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You ask where a book is and they point you to the right aisle.
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An agent is like a personal assistant who has keys to the building.
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You say, "I need information about our top clients for tomorrow's meeting."
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And they go find it, organize it and hand you a briefing document.
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You didn't tell them where to look or what format to use.
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They figured it out, but the secret isn't just the LLM.
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The language model is smart, but by itself it's just a brain with no hands.
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What makes something an agent is the combination of three things.
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Reasoning, the ability to think through a problem.
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Tools, the ability to act on the world.
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And memory, the ability to remember what happened before.
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Put those three together and you have something that doesn't just answer questions,
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it gets things done.
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Before the framework, the fragmented landscape.
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So if agents are so useful, why isn't everyone already building them?
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The short answer is that until recently it was a messy process.
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And you had to stitch together multiple tools yourself,
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kind of like trying to build a car by buying parts from different manufacturers
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and hoping they fit together.
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Microsoft's first attempt at fixing this was semantic kernel,
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an SDK for integrating AI into applications.
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Powerful stuff, but it was a toolbox, not a blueprint.
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It gave you the pieces you still had to figure out how to assemble them.
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Then came Autogen from Microsoft Research.
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This framework explored multi-agent conversations where AI agents talk, debate and collaborate.
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Cutting edge, yes, but it was experimental, not built for production systems.
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So developers had a tough choice, semantic kernel was stable and enterprise ready,
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but lacked the advanced orchestration patterns.
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Autogen had the cool multi-agent capabilities, but wasn't something you'd run in a live environment.
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Each had its own learning curve, its own way of doing things, its own community.
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And it wasn't just Microsoft.
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Other frameworks popped up.
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Langchain, crew AI, doesn't others.
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The ecosystem was all over the place.
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If you wanted to build agents at work,
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you had to become an expert in a rapidly changing world.
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Which framework do you learn?
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Which one will survive?
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Which one is Microsoft actually backing?
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For a beginner, it was overwhelming.
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Imagine you're a developer at a company exploring AI agents.
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You research and find five different frameworks,
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each with its own documentation, its own patterns, its own community.
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You don't know where to start.
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You don't know which one to bet your time on.
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That fragmentation created a real barrier.
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It wasn't just about learning a tool.
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It was about betting on an entire ecosystem.
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And as agent use cases moved from experiments into real business processes,
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Microsoft realized this wasn't sustainable.
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They needed one framework, one unified approach, one answer to the question.
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How do I build an agent?
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What is Microsoft agent framework?
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So Microsoft took the two frameworks they had,
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semantic kernel and autogen, and combined them into one.
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The result is the Microsoft agent framework, a single unified SDK
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that brings together the enterprise stability of semantic kernel
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with the cutting edge multi agent patterns from autogen.
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The teams that built those two frameworks now work together on this one.
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This framework launched in preview in late 2025
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and reached version 1.0 in July, 2026.
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That timeline is important because it tells you
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this isn't a research project or an experiment.
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It's a production-ready product.
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Microsoft is saying, "Build your real systems on this."
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It's also open source.
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You can find it on GitHub with over 7,000 stars and more than 100 contributors.
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The community is active.
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There are weekly office hours where you can talk to the engineering team directly.
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That's not something you get with a closed product.
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The framework supports Python and Benet as first class languages,
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whether you're a CI developer or a Python developer,
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you get the same experience.
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That's a big deal because many AI frameworks are Python only.
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Microsoft made a deliberate choice to treat both languages equally.
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And here's something that might surprise you.
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The framework supports over seven different model providers.
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Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic Cloud, Amazon Bedrock,
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Google Gemini, even local models through Alama.
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You write your agent logic once and it works across all of them.
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You're not locked into one provider,
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switch from OpenAI to Anthropic,
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and you change a configuration value, not your entire code base.
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The framework has two major components.
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Agents are the building blocks,
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individual reasoning units that can think and act.
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Workflows are the orchestration layer that ties multiple agents together.
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You can have one agent doing one job
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or a whole team working on a complex task.
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There's also something called declarative definitions.
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You define an agent's behavior in a YAML file.
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That means your agent configuration is version controllable,
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put it in Git, review it in pull requests,
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deploy it through CI/CD pipelines.
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For enterprise teams, that's a huge advantage.
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But here's what I want you to understand.
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This framework isn't just for developers building complex systems.
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It's designed for enterprise scenarios,
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security, compliance, observability, human in the loop approvals.
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These are built in, not bolted on.
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And that's what makes it different from the experimental frameworks that came before.
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The building blocks, agents, tools, memory, workflows.
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Let's break it down into the four core concepts.
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Get these right, and you understand 90% of what this framework does.
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First up agents, they're the fundamental unit.
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Each agent has a name, a set of instructions, and access to tools.
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It's a reasoning unit that can think and act.
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You tell it who it is, what it should do,
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and what resources it can use.
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Everything else flows from that.
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Next tools, these are the hands of the agent,
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functions, APIs, or external services the agent can call,
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whether data, calendar access, database queries, file operations.
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The agent doesn't know how to do these things by itself.
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It needs tools to reach out into the world and take action.
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You can write simple functions yourself,
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or they can be MCP servers, model context protocol.
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That's a standard way to connect agents to external services.
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Think of it as a universal plug.
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If a service supports MCP, your agent can use it without custom integration code,
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then there's memory.
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This is the brain's storage, two types exist.
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Session level memory is short term.
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It remembers what happened during a single conversation.
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Context providers are long term.
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They remember preferences and facts across multiple conversations.
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Memories what makes an agent feel intelligent.
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Without it, every interaction starts from scratch.
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You introduce yourself, and the next time you talk, it's forgotten.
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With memory, the agent knows who you are,
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what you've discussed, and what you care about.
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It's the difference between talking to a stranger
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and talking to a colleague who knows your work.
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Finally, workflows.
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This is the orchestration layer where you define multiple agents working together.
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Workflows can be sequential.
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Step A, then step B. They can be concurrent.
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Multiple agents working in parallel on different parts of the same problem.
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Or they can be conditional.
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If this happens, go here.
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If that happens, go there.
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Workflows also support check-pointing.
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If something fails halfway through, the system can resume from where it left off.
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It doesn't start over.
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That's important for long-running tasks that might take hours or involve multiple steps.
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Human and the loop patterns are built in too.
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Agents can pause and ask for approval before taking sensitive actions.
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If an agent is about to send an email or approve a payment,
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it can stop and wait for a human to say yes.
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That's essential for regulated industries.
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Here's a real-world example.
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Imagine a sales preparation agent.
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You have a meeting with a client tomorrow.
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You tell the agent, "Help me prepare."
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It checks your calendar to confirm the time.
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It pulls CRM data to see the client's history.
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It searches marketing materials for relevant case studies.
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It generates a briefing document and a PowerPoint presentation.
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Or without you doing anything.
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That's not a chatbot.
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That's an agent.
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Why should you care?
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The practical value.
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So you understand what the framework is.
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But here's the thing.
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Why should you care if you're not a developer-building AI systems?
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The answer is that this framework doesn't just live in a developer's terminal.
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It powers tools that everyday business users interact with.
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Agents built with the framework can be published directly to Microsoft Teams and Copilot Chat.
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That means your organization's agents live where you already work.
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You don't need a separate app to install and you log in to remember or a learning curve.
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You just open Teams and the agent is there.
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For IT and business decision makers, this matters even more.
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The framework provides enterprise controls that actually matter.
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You get identity management through EntraID, Access Control, so agents only see what they're
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supposed to audit trails.
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So you know what every agent did and when and data governance.
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So sensitive information stays protected.
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And here's a problem you might not have thought about.
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Shadow AI is real.
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Agents are using unsanctioned AI tools every day, pasting company data into public chatbots,
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uploading sensitive documents to unknown servers and hoping for the best.
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IT teams know this is happening, but they can't stop it by banning tools.
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People will find a way.
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This framework gives IT a way to offer approved, secure agents that still feel modern.
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Instead of telling employees, don't use AI.
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You can say, use these agents instead.
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They're safe, they're governed, and they work with your data.
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There's also the problem of agents sprawl and it's already happening.
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These are creating agents faster than anyone can track.
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The framework gives you a single place to manage, monitor, and govern all of them.
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You can see which agents are being used, which ones are collecting dust and which ones
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need attention.
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Cost visibility is another big one.
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You can see exactly how much each agent costs to run, which agents are expensive, which
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ones are efficient and where to optimize.
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That's not something you get with a collection of random tools and scripts.
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The bottom line is this.
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This framework makes AI agents practical, safe, and manageable for real organizations.
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There's not just about building cool technology, it's about building something that works in
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a real business with real constraints.
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Security, compliance, budget, governance, if you're responsible for any of those things,
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this framework matters to you.
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How it fits into the bigger picture.
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So where does the agent framework actually fit?
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It's not a standalone product.
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It's one piece of a much bigger puzzle.
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Let's look at how everything connects.
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Start with Azure AI Foundry.
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Think of it this way.
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The agent framework is how you build your agents and Foundry is where they live and run.
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It handles all the heavy lifting, infrastructure, scaling, monitoring, security.
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You build locally, you deploy to Foundry, and your agent runs in a production environment
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with enterprise controls.
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Simple.
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Now, if you're not a developer, you've got another option, co-pilot studio.
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This is the low-code path.
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You use a visual interface to define what your agent does, connect it to data sources, and
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publish it to teams.
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No code required.
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The agent framework is for developers who want full control.
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They complement each other.
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Deep customization, use the framework, need to move fast, use co-pilot studio.
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Then there's Microsoft 365 co-pilot, the user-facing side.
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Agents built with the framework can show up right inside co-pilot.
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So users interact with them through a familiar interface, the same co-pilot they already use
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for work.
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No new tool to learn, no new workflow to adopt.
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But here's where the real value shines, the intelligence layer.
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It has three parts.
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Work IQ connects to your personal data, email, calendar, chats.
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Every IQ connects to organizational knowledge, documents, policies, shared resources, and
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fabric IQ connects to business data.
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Sales numbers, customer records, operational metrics.
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Agents can pull from all three.
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That's what makes them truly useful.
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They don't just answer general questions.
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They have context.
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They know who you are, what you're working on, and what data matters to your business.
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When an agent prepares a briefing document for a sales meeting, it's not guessing.
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It's pulling from your calendar, your CRM, your marketing materials, and your company's
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knowledge base.
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All in one request.
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And it all works with Microsoft's security model.
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Enter ID for identity, intune for device management, defender for threat protection.
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If your organization already uses these tools, the framework fits right in.
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No need to build a separate security layer.
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For organizations already using Microsoft 365, Azure, and Power Platform, this framework
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is the natural next step.
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You're not starting from scratch.
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You're extending what you already have.
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The long term vision?
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Simple.
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Agents become as common as apps.
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Every business process gets an agent layer, not replacing people but helping them work
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faster with better information and fewer repetitive tasks.
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This framework is how you build that layer.
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Getting started, your first step.
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Here's the good news.
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You don't need to be an AI expert to get started.
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There are two paths.
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The no-code path is co-pilot studio.
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Visual interface, drag, drop, configure, no code required.
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You can have something working in hours, not days.
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If your goal is to solve a specific business problem fast, this is your path.
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The code path is the agent framework itself.
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You install the packages for Python or Punch and Net, and you use the VS Code extension.
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And the extension is worth talking about because it includes AI-powered skills.
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You tell it what you want.
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Create a sales prep agent that checks my calendar and pulls CRM data, and it scaffolds
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the entire project for you.
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Code configuration files everything.
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It's not a blank page.
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It's a running start.
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The extension also includes something called the agent inspector.
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This is a visual tool that shows you what your agent is doing in real-time step-by-step.
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You can see it thinking.
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You can see which tools it's calling.
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You can see where it's getting stuck.
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It's like having a debugger that speaks plain English.
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If you're already using semantic kernel or auto-gen, there are migration assistance that
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analyzes your existing code and generates a step-by-step migration plan.
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You don't have to start from scratch.
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The framework is open source.
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The GitHub repo is at GitHub, COM, Microsoft, agent framework.
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Sample's documentation, discussion forums, all there.
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The community is active.
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There are weekly office hours where you can talk to the engineering team directly.
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That's not something you get with most frameworks.
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The key message is this.
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Start small.
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Build a single agent that does one useful thing.
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Maybe it checks your calendar.
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Maybe it summarizes your emails.
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Maybe it looks up customer information.
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See how it feels.
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Then scale up.
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The most important thing is to understand the concepts.
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And that's what this episode was about.
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Let me wrap it up by tying these pieces together.
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An agent isn't a chatbot.
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It's a system that thinks, decides, and acts on its own.
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The framework exists because building agents the old way meant juggling incompatible
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tools and frameworks that didn't talk to each other.
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That fragmentation made everything confusing.
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So Microsoft built one way to build, one way to deploy, and one way to manage.
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No more switching between frameworks or stitching together mismatched pieces.
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For beginners, here's the simple takeaway.
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AI agents aren't magic.
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They're structured systems that reason, use tools, and remember context.
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Microsoft gave you a complete framework to build them.
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And it's production ready right now.
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This isn't hype.
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Agents are becoming a normal part of how we work just like apps and websites did years
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ago.
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The tools are here.
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The community is active, and your first agent is closer than you think.
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If you want to explore more, start with the GitHub repo and the VS Code extension.
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Subscribe on your favorite podcast platform and share this with someone starting their journey.
Founder of m365.fm, m365.show and m365con.net
Mirko Peters is a Microsoft 365 expert, content creator, and founder of m365.fm, a platform dedicated to sharing practical insights on modern workplace technologies. His work focuses on Microsoft 365 governance, security, collaboration, and real-world implementation strategies.
Through his podcast and written content, Mirko provides hands-on guidance for IT professionals, architects, and business leaders navigating the complexities of Microsoft 365. He is known for translating complex topics into clear, actionable advice, often highlighting common mistakes and overlooked risks in real-world environments.
With a strong emphasis on community contribution and knowledge sharing, Mirko is actively building a platform that connects experts, shares experiences, and helps organizations get the most out of their Microsoft 365 investments.
