AI brokers have gotten a key enabler for companies seeking to streamline processes, automate repetitive duties, and empower their staff to work extra effectively. In Microsoft 365 Copilot, we’ve already seen plenty of options that target enhancing productiveness for people. But, the potential for AI-driven automation goes a lot additional when you possibly can join clever , pure language, brokers on to your personal enterprise information and processes—enter Azure AI Agent Service in Azure AI Foundry.
On this put up, you examine why AI Brokers are worthwhile, and the way Azure AI Agent Service makes it straightforward to construct and customise these brokers. I additionally did some testing and share these experiences, together with the Code Interpreter characteristic for information evaluation. Lastly, ultimately you possibly can learn via some fast recommendations on how one can get began.
What Are AI Brokers and Why Ought to Companies Care?Tales of Reworking Enterprise ProcessesAI AgentsAzure AI Agent Service is now in Public PreviewMy Testing Expertise with Code Interpreter and Data SourcesCode InterpreterExternal Data & RAG TestingAgent Configuration Made SimpleModel Tuning Choices and Limitations Getting Began: The Quickstart PrerequisitesCreating and Testing Your AgentExpand with SDK or Extra ToolsConclusionRead extra from these sources:About writing this text
At its easiest, an AI agent is a self-contained “microservice” powered by a big language mannequin (LLM) or comparable AI mannequin. It’s designed to reply questions, carry out actions, and finally automate or increase particular duties. Let’s check out some examples first.
Tales of Reworking Enterprise Processes
A Fictional Have a look at a Multi-Agent Gross sales EcosystemImagine a big electronics retailer that operates in dozens of nations. They’ve separate specialised AI brokers for various points of the gross sales cycle. One AI agent handles lead qualification by scanning incoming inquiries and extracting key details about potential purchasers. Concurrently, one other agent is chargeable for product suggestions primarily based on real-time pricing and stock information. As soon as a lead is certified, a scheduling agent units up demos with gross sales reps and mechanically books a gathering in Microsoft Groups, full with related paperwork connected.
These brokers may also collaborate, passing data to at least one one other about lead standing or the most effective product bundles for a specific area. Because of this multi-agent strategy, a gross sales rep can soar in solely when human interplay is actually wanted, moderately than juggling repetitive duties like re-checking inventory or manually coordinating conferences. The complete course of is a well-orchestrated system that frees staff from repetitive admin and information entry. The end result: extra customized interactions with clients, increased gross sales velocity, and a drastically diminished probability of human error.
Fujitsu: RAG and Gross sales Effectivity within the Actual WorldWhile the above state of affairs is hypothetical, real-life companies already leverage Azure AI Agent Service to revolutionize their gross sales processes. For instance, Fujitsu leveraged Azure AI Agent Service and Semantic Kernel to construct an AI-powered automation resolution to streamline proposal creation, enabling gross sales groups to concentrate on high-value buyer engagement. The AI agent dynamically retrieves and synthesizes information from dispersed sources, guaranteeing accuracy and relevance whereas integrating seamlessly into Fujitsu’s current Microsoft ecosystem. “We’re utilizing Microsoft’s Semantic Kernel and Azure AI Agent Service to orchestrate a number of specialised AI brokers and an orchestrator AI to coordinate them to reply questions as a crew,”
Cineplex: Reworking Buyer Service Via AutomationCineplex, a number one Canadian media and leisure firm, has reworked its customer support operations utilizing AI-powered automation. Certainly one of its largest challenges was dealing with refund requests, a time-consuming course of that took 5–quarter-hour per request. To unravel this, Cineplex carried out an AI Copilot agent utilizing Microsoft Energy Platform and Azure AI. Now, visitor service brokers merely enter a reserving ID and date, and the AI handles the remaining—retrieving information, validating the request, and finishing the refund in about 30 seconds This highlights how AI brokers can optimize routine duties, increase effectivity, and improve customer support—with out changing human interplay. For companies seeking to scale assist operations, AI-powered automation generally is a game-changer.
AI Brokers
Not like fundamental chatbots, AI brokers can incorporate context from historic conversations and hook up with exterior programs, permitting them to:
Search your organization’s information base or the online.
Course of and interpret information or real-time information.
Make calculations, generate stories, even run code.
Carry out advanced duties that save staff effort and time.
Combine with different programs, inside and exterior
Have interaction different brokers within the course of: multi-agent programs
Cope lots higher with numerous conditions higher than conventional automation. This is because of LLM of their “core” giving understanding of the purpose and what’s wanted. AI Brokers are versatile and might adapt to conditions – and likewise know when to ask assist from an one other agent or an actual individual.
In different phrases, AI brokers can complement human staff by taking up repetitive or time-consuming jobs. Which may imply a buyer assist agent that mechanically retrieves solutions from a information base, an inside finance agent that crunches funds information from spreadsheets, or a gross sales agent that triggers e mail workflows.
From a enterprise standpoint, AI brokers have tangible advantages:
Accelerated decision-making: Dynamic, context-aware AI reduces guide analysis.
Scalability: Brokers can work across the clock, dealing with duties for a number of groups concurrently.
Consistency: They apply information and logic in a uniform method—fewer errors attributable to human error. At this time is the time after we experiment with brokers, and it must be realized brokers may also make errors – typically even loads.. The purpose is within the consistency and developing with new concepts the place AI can remodel the method, and these gained’t be reached with out experimenting, developing with difficult use instances and braveness to check out one thing new.
Azure AI Agent Service, now out there as public preview within the Azure AI Foundry portal, gives a managed surroundings to construct, debug, and deploy these AI brokers. It’s designed in order that builders and tech-savvy enterprise customers can rapidly form an agent’s capabilities with out having to assemble all of the underlying code or infrastructure themselves. This accelerates pro-code agent improvement and is but an one other instance of fusion groups the place enterprise and builders work collectively.
Key capabilities embrace:
Prepared-to-Go Instruments & Integrations:
Code Interpreter: Permits brokers to execute Python code inside a safe sandbox—nice for number-crunching, information evaluation, or producing graphs.
Bing Search & Azure AI Search: Brokers can pull in exterior information from the online or your personal information, including related context to duties.
Azure Capabilities Assist (SDK-based): Builders can expose customized enterprise logic or exterior APIs to the agent, letting it set off real-world actions.
Conversational Reminiscence:Brokers can preserve a thread of dialog, keep in mind particulars, and proceed the place you (or the agent itself) left off. That is dealt with securely on the server aspect.
A number of Mannequin Choices:Though Microsoft’s GPT-4o is a well-liked alternative, you may as well deploy different accomplice fashions like Cohere or Mistral within the Azure AI Foundry. (Notice: Mistral-large-2407 is changing into legacy and might not be out there for much longer.)
Primary vs. Normal Setup:
Primary Setup (supported within the Azure AI Foundry portal in the present day): You depend on Microsoft-managed sources for storage and search. Fast to begin, minimal overhead, however it gives much less management.
Normal Setup (Bicep template–solely): You deliver your personal sources (like Azure Storage and Azure AI Search) for full visibility and price administration.
I’ve spent a while exploring the brand new Brokers UI in Azure AI Foundry, placing these options to the take a look at. Right here’s what stood out for me:
Code Interpreter
A enjoyable (although fictional) state of affairs was exploring the terminal velocity of a laptop computer falling from an airplane. With the agent’s Code Interpreter device enabled, I may ask the agent to run physics-related calculations. It may possibly generate fast math scripts in Python—and that is only a easy instance in regards to the Code Interpreter.
I uploaded an Excel file for the 2023 funds of the town of Vantaa (out there as open information) to the Code Interpreter. The agent then learn and interpreted the file, making it easy to research funds figures, collect insights, and visualize the info.
Exterior Data & RAG Testing
I additionally examined a state of affairs utilizing fundamental RAG (retrieval augmented technology). By importing some demo paperwork, the agent was in a position to pull focused information from my very own content material, weaving them into its solutions. The RAG with AI isn’t something new anymore, however the Assistants API working behind the hood isn’t an on a regular basis device but. So it made sense to mess around to see the way it performs – and it was similar to I anticipated.
Agent Configuration Made Easy
The visible circulate within the Azure AI Foundry UI is deceptively easy: outline your agent’s identify, add Data sources (information or indexes), and specify which Actions (instruments) the agent could use.
At the moment, the one motion out there from the UI is Code Interpreter. If you wish to combine your personal, corresponding to Azure Capabilities, you are able to do extra by way of the SDK.
Mannequin Tuning Choices and Limitations
Primary tuning for Temperature and Prime P is well accessible within the UI, so you possibly can modify how artistic or deterministic your agent’s solutions needs to be.
Within the UI, solely Code Interpreter is displayed as an “motion,” however the underlying Assistants API definition is versatile—new actions or customized instruments might be added as soon as they’re enabled.
I experimented with GPT-4o, which labored seamlessly. The service additionally guarantees assist for non-OpenAI fashions like Cohere and Mistral, although my free Azure subscription didn’t permit me to deploy them.
General, these assessments spotlight how rapidly you possibly can piece collectively a specialised AI agent that’s distinctive to your model, crew, or undertaking. With a couple of traces of code or a couple of clicks within the UI, you possibly can remodel a easy chat mannequin right into a mini-assistant with actual enterprise worth.
Wish to strive it your self? Right here’s a brief information primarily based on the official quickstart.
Stipulations
An Azure subscription (create a free trial if wanted).
The Azure AI Developer function assigned. This provides you the correct permissions to create and handle AI brokers.
Primary Setup by way of the Azure AI Foundry Portal: As a result of the Foundry portal solely helps the “fundamental setup,” you’ll be utilizing Microsoft-managed storage and search behind the scenes. This will get you getting began quick.
Creating and Testing Your Agent
Navigate to Brokers within the Azure AI Foundry portal and choose “New agent.”
Present a reputation and add directions (e.g., “You’re a enterprise analyst specializing in forecasting.”).A tip: use Chat Playground’s Generate immediate characteristic to construct directions for the agent.
Below “Data & Motion,” add Code Interpreter if you’d like the agent to deal with information evaluation or code execution. You can too connect as much as 20 information that your agent can learn and use for producing outputs.
After configuring your agent, change to the “Playground” to start chatting.
You may revise directions, tweak mannequin parameters (Temperature, Prime P), or add new information information and instruments.
Affirm that your agent is responding as anticipated and refine your directions or information sources if wanted.
To include your personal Azure Capabilities or exterior APIs, you’ll have to outline them as instruments by way of the Azure AI Foundry SDK or the Azure OpenAI SDK. That is significantly useful for extra advanced automations the place the agent would possibly, as an illustration, replace a CRM report or ship an e mail in your behalf.
Azure AI Agent Service is a promising step ahead in automating numerous enterprise processes—from information evaluation and RAG queries to extra action-oriented duties like connecting to exterior APIs. The mix of enormous language fashions, built-in instruments, and easy setup within the Azure AI Foundry UI makes it a compelling alternative for attempting out a wide range of automation situations. Sooner or later (close to, I hope) we are able to additionally add multi-agent programs to this.
For enterprise determination makers, one key issue is how rapidly and securely it’s doable obtain operational advantages (and ROI). Whether or not you’re in finance, manufacturing, retail, or past, AI brokers supply a brand new method to faucet into supercharging enterprise processes. Assume scaling processes that historically rely on human intervention, to agent-driven that enhance productiveness, scale back guide errors, and liberating tedious work (and treasured) time from people. After I speak with clients about Microsoft 365 Copilot, it already helps many to finish extra duties sooner than earlier than. For a lot of of them, which means much less lengthy days turning evenings – or that pile of to do duties stays in management.
When you’re curious, I like to recommend checking the quickstart, spinning up a fundamental agent, and giving Azure AI Service UI and particularly the Code Interpreter with Assistants API a strive. From information crunching to serving to your gross sales or assist groups, you possibly can see how briskly you possibly can construct an pro-code agent core able to significant work.
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Sure, I used once more the Azure OpenAI Service reasoning mannequin o1 to assist me out with this. I offered the mannequin a protracted immediate, that included my purpose, insights, data of what I did and what I wished to specific within the put up. Together with the background data from Microsoft Study and articles. After that I used some prompts to refine the end result and added instance use instances. Lastly I coped the textual content to the weblog and went via this – making use of adjustments, deleting elements and including new insights, and naturally footage. This sped up the precise weblog writing course of quite a bit, however it nonetheless took a couple of of hours in complete.
Maybe for one weblog put up I’ll create a Groups assembly, that I report and transcribe, once I testing out new characteristic. Utilizing that might present fairly an distinctive base for the put up draft, that I generate with the assistance of o1. That might not be so structured as writing my chosen insights, however would undoubtedly be a distinct method. Will it’s sooner? That I can discover out by testing it out.
