What Exactly Are AI Agents—And What’s New?
Let’s keep it simple: AI agents are your digital team members, powered by large language models, that tackle those “why-am-I-doing-this-again?” jobs nobody wants and much more. They learn from interactions, tap into both internal and external data, run code on the fly, access other applications and resources, and can even trigger external APIs—like digital assistants with serious muscle.
What’s new? Modern AI agents aren’t just glorified chatbots anymore. They can:
- Pull in context from previous chats or external data
- Run code (thanks to cool stuff like the Code Interpreter),
- Integrate with your business apps (CRM, ERP, custom APIs, or other Azure-based apps),
- Team up with other AI agents to tackle multi-step workflows.
A Few Examples:
Agentic AI systems are already showing up in a variety of real-world scenarios. In customer service, they’re proactively resolving queries—detecting issues like delayed orders, offering discounts, and pulling data from countless sources to fix problems before humans even notice. In manufacturing, these AI-driven “virtual factories” monitor production lines, predict maintenance needs, and even suggest design tweaks to boost efficiency and quality. For sales support, agentic AI takes on the grunt work—handling emails, setting up meetings, responding to customer questions—so sales teams can spend more time closing deals. And as a last example, in health and social care, agentic AI can serve as empathetic virtual caregivers, guiding patients through procedures, managing medications, or just being a friendly check-in partner. Across these use cases, the common ground is greater autonomy and proactivity, enabling faster responses, specialized expertise, and a more seamless human–machine collaboration.
Azure AI Agent Service (in public preview)
Now, how do you build and manage these AI super-assistants without needing a PhD in machine learning? That’s where Azure AI Foundry comes in. It’s basically your workshop for designing, testing, and deploying AI agents with minimal fuss.
Ready-to-Go Tools & Integrations
1. No Heavy Lifting: Skip wrestling with servers and storage—spin up an agent in minutes using Microsoft-managed resources.
2. Code Interpreter: Run Python in a sandbox to crunch numbers, analyze data, or generate insights—all from the agent itself.
3. Use your Data or Search the Internet: Plug in your existing knowledge bases or connect external sources (like Bing Search or Azure AI Search).
4. Easy Expansion: Need your agent to do more? Add custom actions (via Azure Functions) or tie it into your CRM, ERP, or other business systems.
5. Conversational Memory: These agents remember previous interactions and details, so you can pick up exactly where you left off. It’s all handled server-side, so your conversation history stays consistent.
6. Model Options: Choose between OpenAIs models or the ones from Meta or Mistral among others. Your can just simply choose and deploy inside Azure AI Foundry.
A quick word on the Setup: Basic vs. Standard:
Basic Setup for easy, Microsoft-managed resources or the Standard Setup for hands-on control, visibility, and cost management. Perfect if you’re big on governance.
My Impression of the Agents Service
I spent some time experimenting with this, and I’m really impressed by how easy it is to build these agents within your Azure environment. Although it’s still in preview and missing a few important details, it’s already evident how plug-and-play the future of AI agents on Azure will be.
By uploading a demo document containing the 2024 sales data for a fictional bike shop—and enabling the Code Interpreter tool—I was able to test both RAG (retrieval augmented generation) and the Code Interpreter’s ability to generate plots on the fly. For reference, setting this up with code would usually take me about an hour. Check out how much faster I managed it in the video below.
Breakdown of what I did:
- Create an agent in one click
- Setup is minimal:
- My model was already deployed so it automatically appeared there. You can deploy others in less than a minute and select them.
- Gave it its instructions so it knows what to do.
- Selected my text file containing the demo data, and it got indexed automatically to be used right away.
- Activated the Code Interpreter capability.
- Left the model settings (temperature and top p) as they are.
- Selected go to playground to test my agent.
- Prompted what to do and got the agent to research the data i gave him and make a barplot to show the results.
Here’s the data I used in case you want to reproduce this demo:
Conclusion
Azure AI Agent Service truly changes the game by enabling users to automate everything from spreadsheet analysis to RAG-driven data retrieval—all without the hassle of managing complex infrastructures. Thanks to the intuitive setup in Azure AI Foundry (plus more advanced options via the SDK), even non-technical users can prototype a specialized agent for a single task and then deploy it into a business application, creating tangible value in record time. This rapid prototyping slashes development costs, speeds up ROI, and makes it simple to pivot quickly from idea to working solution. We encourage you to explore the service firsthand, inspired by the straightforward resource access showcased in the diagram shown above, and discover all the possibilities Azure AI Agent Service can unlock for your own business. Ultimately, that’s what makes it such a powerful catalyst for innovation and productivity in any enterprise environment.