Agentic Table of Contents (so far)

1 RAG – A quick example

2 RAG in detail

3 Deeper into RAG

4 The R in RAG

5 PEAS for Agentic AI

6 AI Reasoning and Planning

7 REACT and Agents in AI

8 Creating a multi-agent system (Python & LangChain/LangGraph) part 1 of 6

14 Migrating Agentic Code Python -> C# Part 1 of 6

20 Dependency Injection & Microsoft Agentic Framework

21 Migrating C# to Microsoft Agentic Framework

22 Transparency in Agentics

23 Ensuring Agent Safety

24 Logging & OpenTelemetry in Microsoft Agentic Framework

25 Activity Source in Microsoft Agentic Framework

26 Middleware in Microsoft Agentic Framework

27 Limit token usage in Microsoft Agentic Framework

28 Using OpenTelemetry in Microsoft Agent Framework

29 LangChain/LangGraph vs. Microsoft Agent Framework

30 Long term memory in Microsoft Agent Framework

31 Agent vs ChatClient Middleware

32 Managing Secrets

33 Microsoft Agent Framework and Foundry

34 Agent Memory for .NET

35 About Neo4j

36 Reducing Token Usage

37 Deploying Microsoft Agent Framework applications to Foundry

38 Copilot Harness and Microsoft Agent Framework

39 GitHub SpecKit

40RAG in Microsoft Agent Framework

41 Executors and Edges

42 Deploying a Microsoft Agent Framework application to Foundry

43 Implementing the Move to Foundry

44 Agent Skills 101

45 Microsoft Tenants

46 Microsoft Agent Framework Deep Dive Podcast

47 Copilot Review

48 Session State

49 Human In The Loop

50 Spec-Driven Development

Note: many of these blog posts had initial research and drafts done by the Blog Writer multi-agent application that serves as a demo for these articles. All were then edited by me.

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Spec Driven Development (SDD)

I am totally blown away by the combination of Spec Kit and Copilot. It is one thing to read about SDD and quite another to build with it.

[For more on the steps in SDD, please see what I wrote about Spec Kit previously.]

Today I fired up Spec Kit and pointed Copilot at it, instructing Copilot to create a constitution based on the existing code (my blog writing demo). A minute later I had a comprehensive document that listed all the immutable characteristics of the application. It was quite long, but here is the beginning:

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Zoom Presentation

Click here

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Important for programmers to remember

No one ever wanted a ⅛ inch drill bit. What they want is a ⅛ inch hole.

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Microsoft Agent Framework: Human In The Loop

tl;dr
If you’re building automated workflows that sometimes must pause for human judgment (approvals, moderation, triage), Microsoft Agent Framework provides first‑class HITL primitives: typed RequestPorts, durable suspended execution, emitted RequestInfoEvents, and automatic routing of responses back to the correct workflow executor.

This article explains the runtime mechanics (what happens when a workflow asks a human for input), the developer experience (what you write), concrete examples (C# + JSON contracts), integration patterns with Bot Framework/contact centers, failure modes, and testing strategies.

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Using Session State

My demo program is a multi-agent application that accepts a topic BlogAgent, researches that topic (ResearcherAgent) and then drafts a blog post (AuthorAgent). The post is then reviewed by another agent (ReviewerAgent) who either accepts the draft or sends it back to the Author with requested changes.

I wanted to allow the user to refine the search after getting the draft. To do so, I needed short term memory. Here are the changes I made…

I implemented the search refinement flow by updating the research state and wiring it into the research step:

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Copilot Review To The Rescue

For those who are following along with the evolution of my Blog Writing application: after the last revision (allowing follow-up questions to a query) Copilot evaluated my Pull Request and sent me an email that the follow-up was not, in fact, being added to the research stage. This was because the original research was in place and so that step was skipped.

Copilot and I fixed this with the following changes:

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Podcast: MAF Deep Dive

Happy to post to my podcast a deep dive into Microsoft Agent Framework with Daniel Costea. Available here or wherever you get your podcasts.

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Microsoft Tenants

What is an Azure tenant (Microsoft Entra ID tenant) — and what is it used for?

tl;dr
An Azure tenant — now called a Microsoft Entra ID tenant — is a dedicated, isolated instance of Microsoft’s identity service that represents an organization’s identity and access boundary. It’s the directory that stores users, groups, applications and their credentials, service principals, roles, and policies used to authenticate and authorize access to resources and apps.

Why the distinction matters
People often confuse “tenant” with “subscription.” They are different:

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Agent Skills 101

tl;dr
Imagine an AI assistant in your company that can write emails, check expense reports, and run CI pipeline steps — but it has every capability baked into one giant prompt. It’s slow, expensive, brittle, and dangerously able to access systems it shouldn’t. Now imagine the same assistant composed from many small, well-documented, auditable “skills”: a policy-checker skill for expenses, an email-composer skill, a CI-invoker skill. Each skill advertises a compact summary the agent sees up-front and provides richer instructions or safe code only when needed. That’s the difference agent skills make: modularity, safety, and maintainability.

In this post you’ll get:

  • A practical definition of agent skills and why they matter.
  • The SKILL.md packaging convention and how agents discover and use skills.
  • Runnable C# patterns for semantic (prompt) functions and native (C#) functions that agents can call.
  • A small retrieval-augmented (RAG) example (embeddings + local vector store).
  • Concrete operational guidance: progressive disclosure, security, testing, telemetry and CI.
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Deploying to Foundry

The demonstration program discussed (and used) in the previous blog posts ran entirely locally as a console application. This week I set out to migrate it to Foundry. To do so, I enlisted the help of Bruno Capuano and Jon Galloway, both of Microsoft, and, of course, Copilot, as documented in these videos (part 1 and part 2).

The change log is here. The architectural design can be found here. The complete demonstration solution is here.

The tl;dr is that we created projects for each of the four agents and deployed them individually to Foundry. The rest of the code stayed on the local machine. Each agent is independently deployed and each has its own managed compute and its own Entra ID, exposing an OpenAI-compatible /responses endpoint (as explained in the architectural design document).

The demonstration program will continue to evolve, but this marks a significant milestone… let’s call it version 3.0 (1.0 was LangChain/Python, 2.0 was the port to Microsoft Agent Framework/C#)

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Copilot Code Review

tl;dr
Copilot code review gives every pull request a fast, consistent first pass — catching typos, missing null checks and small bugs so human reviewers can focus on architecture, design and higher‑risk decisions.

What is Copilot code review?
Copilot code review is an AI‑powered reviewer that runs inside your pull‑request workflow (GitHub and integrated Azure DevOps/Azure Repos). It reads the PR diff (and respects repository custom instructions, if configured), posts inline comments as the user “GitHub Copilot,” and — where applicable — offers suggested fixes you can apply as commits directly from the PR UI. The goal is a fast, consistent first pass on correctness, style, security and maintainability so humans can spend their time on higher‑value review work.

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Migrating Agents to Foundry

I wanted to deploy my demonstration program, a local console application, to Foundry. The short answer is that without a front end (e.g. a Blazer Application) it made sense only to host the agents (and the tool) on Foundry, and leave the actual application on my local machine.

To do this, I consulted with Bruno Capuano and Jon Galloway, both of Microsoft. The result is a video that you can find here: Microsoft Agent Framework and Foundry Part 2.

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