Building AI Agents: A Practical Framework for Getting Started

Hey, you’ve probably been hearing about "AI agents" everywhere by now, right? Every tech announcement, every product launch, every conference talk. But here’s the thing: most explanations either dive too deep into academic theory or stay so high-level that you’re left wondering "okay, but how do I actually build one?"

I’ve been experimenting with AI agents for a while now, and in this post, I’d walk you through a practical framework for understanding and building them. We’ll stay mostly in the Microsoft ecosystem and open source since that’s where I spend most of my time, but the concepts apply broadly.

What Even Is an AI Agent?

Let me put it simply: an AI agent is software that can take actions on your behalf, not just answer questions.

Think about it this way. ChatGPT is great at answering "how do I book a flight?" But an AI agent would actually go ahead, search for flights, compare prices, check your calendar for conflicts, and book it for you. That’s the difference between a chatbot and an agent.

The industry is calling this the "Actionable Agents" phenomenon, and it’s probably the biggest shift happening in AI right now. We’re moving from AI that talks to AI that does.

The Core Components: What Makes an Agent Work

Every AI agent, whether you’re using AutoGen, Semantic Kernel, or building from scratch, has these core pieces:

1. The Brain (LLM)

This is your foundation model – GPT-4, GPT-5, Claude, whatever you prefer. It handles reasoning, understanding context, and deciding what to do next.

2. Memory

Agents need to remember things. There’s two types:

Working memory: What’s happening right now – the current task, recent observations, conversation history
Long-term memory: Past experiences, learned preferences, successful strategies

Without memory, your agent would be like that colleague who forgets every conversation you’ve ever had. Frustrating.

3. Tools

This is where agents get their superpowers. Tools are the APIs, functions, and services your agent can call. Search the web, query a database, send an email, create a file – these are all tools.

4. Planning and Reasoning

The agent needs to figure out: "Given this goal, what steps should I take?" This is where patterns like ReAct (Reason-Act) come in, but more on that later.

The Microsoft Ecosystem: What’s Available

If you’re in the Microsoft world like me, here’s what you’ve got to work with:

Azure AI Foundry Agent Service

This went GA in late 2025 and it’s Microsoft’s managed runtime for AI agents. You can build, deploy, and manage agents without dealing with infrastructure headaches. It supports multi-agent workflows, persistent memory, and integrates nicely with Microsoft 365 tools.

The cool part? It supports the Model Context Protocol (MCP) out of the box, which means your agents can connect to external tools and services through a standardized way.

The Microsoft Agent Framework (AutoGen + Semantic Kernel)

Here’s something interesting that happened in October 2025. Microsoft merged two of their biggest open source AI projects:

AutoGen: Great for building multi-agent systems with lightweight orchestration
Semantic Kernel: Enterprise-grade plugins, middleware, and conversation management

The result is the Microsoft Agent Framework – and it’s fully open source under MIT license.

What this means for you: you get AutoGen’s flexibility for agent-to-agent communication combined with Semantic Kernel’s battle-tested plugin system. Best of both worlds.

Quick Example: Setting Up a Basic Agent

Here’s what a simple agent looks like using the Python SDK:

from autogen_ext.models.semantic_kernel import SKChatCompletionAdapter
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion

# Set up your LLM connection
kernel = Kernel()
kernel.add_service(AzureChatCompletion(
    deployment_name="gpt-4",
    endpoint="your-endpoint",
    api_key="your-key"
))

# Wrap it for use with AutoGen
llm_client = SKChatCompletionAdapter(kernel)

# Define your agent with tools
agent = AssistantAgent(
    name="research_assistant",
    model_client=llm_client,
    tools=[search_web, read_file, summarize_text]
)

Nothing too scary, right?

Architecture Patterns: How Agents Think and Act

Now let’s get into the interesting part – how agents actually work. There are a few patterns you’ll see everywhere:

Pattern 1: ReAct (Reason, Act, Observe)

This is probably the most common pattern. The agent:

Reasons about what to do
Acts by calling a tool
Observes the result
Repeats until done

It’s flexible and handles unexpected situations well. Use this when your task might go sideways and need mid-course corrections.

Pattern 2: Plan-and-Execute

The agent creates a complete plan upfront, then executes it step by step. More efficient if you know the environment is predictable.

Think of it like: ReAct is GPS with real-time traffic updates, Plan-and-Execute is printing directions before you leave. Both work, depends on your trip.

Pattern 3: Multi-Agent Teams

This is where things get interesting. Instead of one agent doing everything, you have specialized agents working together:

A Planner agent breaks down goals into tasks
An Executor agent runs the tools
A Verifier agent checks the results
A Manager coordinates everyone

Microsoft’s Magenta architecture in Azure AI Foundry is built around this pattern. It’s more complex to set up but handles sophisticated workflows much better.

Model Context Protocol (MCP): The Open Standard You Should Know About

Let me tell you about MCP because this is important for anyone building agents.

MCP is an open protocol (started by Anthropic, now maintained by the Linux Foundation) that standardizes how agents talk to tools. Think of it like USB for AI agents – instead of building custom integrations for every tool, you follow MCP and things just work.

Why does this matter?

Your agent can discover and use any MCP-compatible tool
You can build one tool integration and have it work with any MCP-compatible agent
Major players have adopted it: Microsoft, OpenAI, Google DeepMind

In Azure AI Foundry, MCP support is built-in. You can connect to remote MCP servers for things like Slack, GitHub, databases, whatever you need.

A Practical Framework for Building Your First Agent

Alright, let’s get practical. Here’s my recommended approach:

Step 1: Start Simple – Single Agent, Few Tools

Don’t jump into multi-agent systems on day one. Build a single agent with 2-3 tools and a clear purpose.

For example: "An agent that can search my SharePoint, summarize documents, and answer questions about them."

Step 2: Get Your Planning Pattern Right

For most use cases, start with ReAct. It’s forgiving of mistakes and handles real-world messiness.

Step 3: Add Memory When Needed

If your agent needs context across sessions or should learn from past interactions, add persistent memory. Azure AI Foundry Agent Service handles this for you.

Step 4: Scale to Multiple Agents Only When Necessary

Multi-agent systems are powerful but complex. You’ll deal with coordination, state management, conflict resolution. Only go there when a single agent genuinely can’t handle the workload.

Step 5: Implement Verification

Always have some way to check your agent’s work. In production, this could be:

A separate verifier agent
Human approval gates for high-stakes actions
Automated testing against expected outputs

Some of the Gotchas

While building agents is exciting, it’s not all perfect. Here are a few things you might experience:

Token costs add up fast. Agents that reason through multi-step problems consume a lot of tokens. Watch your bills, especially during development.
Hallucination doesn’t disappear. Your agent can still make things up. Tools help ground responses in real data, but you need verification.
Tool descriptions matter more than you think. If your tool descriptions are vague, the agent won’t know when to use them. Be specific.
71% of agent projects never reach production. According to a recent Camunda report, trust and governance issues kill most deployments. Plan for this from the start.
Security is tricky. Agents that can take actions can take wrong actions. Implement proper permissions, logging, and rollback capabilities.

What’s Coming Next

The industry is moving fast. Here’s what I’m watching:

Agent 365: Microsoft’s play to put agents everywhere in Microsoft 365. They’re projecting over a billion agents in use by 2028.
Agentic Commerce: AI agents that shop for you. Morgan Stanley projects $115 billion in additional e-commerce by 2030 from this.
Agent Identity (Microsoft Entra Agent ID): Giving agents their own identity in your directory, so you can manage permissions and track what they do.

Getting Started Today

If you want to dive in, here’s what I’d recommend:

For learning: Start with the AutoGen tutorials on GitHub. They’re well-documented and cover common patterns.
For prototyping: Use Azure AI Foundry’s playground. You can test agent concepts without writing much code.
For production: Azure AI Foundry Agent Service gives you the governance, security, and scalability you’ll need.
For staying current: The Microsoft Agent Framework GitHub repo is where the action is. Watch it for new patterns and capabilities.

That’s It

Building AI agents isn’t rocket science, but it does require understanding the core patterns and picking the right tools for your use case. Start simple, iterate, and add complexity only when you need it.

The era of AI that just talks is ending. The era of AI that acts is here. The question is: what will you build?

Do let me know in comments about your experience building agents. I’d love to hear what patterns and tools are working for you.

Happy building, folks!

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