AI agents are considered the next step in automation: they operate autonomously, learn from context, and can be seamlessly integrated into business processes. Creating an AI agent may sound abstract at first, and it can be difficult to know where to start. That’s why we’ll break down which platforms are best suited for this purpose and why.
We compared four leading solutions: Azure AI Foundry, Make, n8n, and Relevance AI. Our focus wasn’t just on traditional criteria like user interface or integrations—but on real-world applicability in a business context and the question: Which platform helps you establish AI agents as a sustainable part of your business solutions?
What exactly is an AI agent platform?
At its core, it’s about more than just automation. AI agents…
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act in a focused manner,
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integrate into complex system environments,
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use LLMs, APIs, data sources, and tools simultaneously
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and can be integrated into larger business processes as reusable building blocks.
The key difference from traditional automation platforms lies in its ability to plan, adapt, and make decisions independently.
Four platforms – four philosophies
| Category | Azure AI Foundry | Make | n8n | Relevance AI |
|---|---|---|---|---|
| Focus | Enterprise Agents in the Microsoft Ecosystem | No-Code Workflows for Business Teams | Open-Source Automation for Developers | Insight-driven AI agents |
| Agent Abilities | 🢢 Tools, Memory, Multi-step Reasoning | ❌ Linear logic only | ❌ Not a true agency model | 🟢 Agent Orchestration & Feedback |
| LLM Integration | 🟢 Azure OpenAI, Cognitive Services | 🟡 HTTP API for GPT | 🟢 OpenAI, HuggingFace, Azure | 🟢 Native LLMs + vector databases |
| API/JSON Handling | 🟡 Designed for developers | 🢢 Visual JSON Mapping | 🟢 Powerful API flows | 🟡 Focus on prompts and code |
| Scalability | 🟢 DevOps, Testing, Access Control | 🟡 Scenario level only | 🟢 Modular, Git-compatible | 🡢 SaaS-only, limited embedding |
| Enterprise Fit | 🟢 Governance, Multi-tenant, Access Zones | ❌ No governance layer | 🟡 Self-hosting is possible | 🟡 Good AI, but not a perfect fit for the platform |
Our assessment: What is the best way to create an AI agent?
Azure AI Foundry
For companies looking to strategically embed AI agents into Microsoft technologies, Azure AI Foundry is the top choice. From Copilot Studio to Power Platform and Dynamics 365—agents can be orchestrated securely and at scale here. Governance, lifecycle management, and multi-tenant capabilities are included.
Limitations: Still in development; technical UX; primarily for developers.
Make
If you want to automate simple processes—for example, in marketing, HR, or customer support—Make is an excellentSolution. JSON handling, API connections, and visual mapping are all implemented intuitively.
Limitations: No agent model, no enterprise architecture, limited reusability.
n8n
Open-source, flexible, developer-friendly—n8n stands out with its robust API logic and custom code capabilities. n8n can be effectively integrated, particularly asSolution specific agent processes.
Limitations: No native agent focus, lack of governance features.
Relevance AI
Ideal for insight-driven agents, such as those in customer service or product feedback. Relevance AI combines LLMs, vector data, and agent control in a streamlined interface—though with a focus on specific use cases.
Limitations: Not designed for broader business architectures or deep integration with business suites.
Conclusion: The ability to create an AI agent is becoming critical to a company’s survival—but not every platform is designed for this purpose
Anyone seriously considering scalable, testable, and controllable agents in an enterprise context should not be blinded by a simple interface or a short-term use case. The key question is:
Can I Solution , update, test, and further develop this agent later as part of my Solution —just like any other business module?
If the answer is “yes,” Azure AI Foundry is currently the most promising option—especially for companies operating in the Microsoft ecosystem. Those looking to get started more quickly with smaller use cases will find interesting alternatives in Make, n8n, or Relevance AI. However, true business maturity only comes with structure, governance, and a platform strategy.
Want to do more than just try out AI agents—want to use them strategically?
As a Microsoft partner for Data & AI, we’ll help you get started—from tool selection and architecture consulting to implementation. Contact us for a personalized assessment.
