Choosing the Right Platform for Building AI Agents

Building an AI agent from scratch gives you complete control over how it works. Instead of relying on predefined workflows or platform limitations, you decide how the agent reasons, stores information, communicates with other systems, and interacts with users. Although this approach requires more development effort, it provides the flexibility needed for highly specialized solutions.

For organizations with experienced development teams, custom development is often the preferred choice when existing platforms cannot satisfy business or technical requirements.

Popular technologies

• Python
• C# / .NET
• Java
• JavaScript / TypeScript
• OpenAI SDK
• Anthropic SDK
• Google Gen AI SDK
• Model Context Protocol (MCP)

Why choose custom development?

• Complete architectural freedom
• Unlimited customization
• No vendor lock-in
• Freedom to select models and libraries
• Easy integration with existing systems

Things to consider

• Longer development time
• Requires AI and software engineering expertise
• You are responsible for security, scaling, monitoring, and maintenance

Ideal use cases

• Custom business workflows
• Complex enterprise applications
• Specialized integrations
• Products that require unique AI capabilities

Summary

Custom development delivers the greatest flexibility and control, making it ideal for organizations that have the technical expertise to build and maintain their own AI solutions.

Many organizations already use business platforms that now include built-in AI capabilities. Rather than creating an AI agent from scratch, these platforms allow you to build intelligent assistants directly within existing business applications. This significantly reduces development effort because the agent can immediately access business data, workflows, permissions, and automation features that already exist.

For companies that are heavily invested in a specific business ecosystem, this is often the fastest path to delivering useful AI solutions.

Leading enterprise platforms

• Salesforce Agentforce
• ServiceNow AI Agent
• SAP Joule Agents
• Microsoft Dynamics 365 Copilot
• Oracle AI

Key advantages

• Native integration with business applications
• Enterprise-grade security and governance
• Built-in workflows and automation
• Faster implementation
• Vendor support and maintenance

Things to consider

• Higher licensing costs
• Less architectural flexibility
• Potential vendor lock-in
• Features depend on the platform

Ideal use cases

• Customer service
• IT service management
• HR automation
• Finance operations
• Enterprise workflow automation

Summary

Enterprise AI platforms focus on speed, reliability, and business integration. They are an excellent choice when AI should enhance existing enterprise applications rather than replace them.

Not every AI solution requires a team of developers. Low-code and no-code platforms allow users to build AI-powered workflows through visual interfaces instead of writing large amounts of code. By connecting triggers, actions, and AI models, organizations can automate everyday tasks in a fraction of the time needed for traditional development.

These platforms are especially useful for creating internal automations, prototypes, or business processes that integrate multiple applications. While they may not offer the same flexibility as custom development, they provide an excellent balance between speed and functionality.

Popular platforms

• Zapier Agents
• n8n
• Microsoft Power Automate
• Microsoft Power Platform
• Make (formerly Integromat)

Key advantages

• Little or no programming required
• Fast development and deployment
• Hundreds of built-in integrations
• Easy workflow automation
• Suitable for rapid prototyping

Things to consider

• Limited customization for complex scenarios
• Advanced workflows can become difficult to maintain
• Platform capabilities differ

Ideal use cases

• Business process automation
• Connecting multiple applications
• AI assistants for internal teams
• Personal productivity workflows
• Proof-of-concept projects

Summary

Low-code platforms make AI accessible to a much wider audience. They are an excellent choice when speed, simplicity, and integration are more important than complete technical control.

Cloud providers offer complete ecosystems for building, deploying, and managing AI agents. Instead of assembling individual components yourself, these platforms combine AI models with infrastructure, security, storage, monitoring, and development tools. This allows development teams to focus on building intelligent solutions rather than managing servers and infrastructure.

Cloud platforms are particularly attractive for organizations that already run their applications in the cloud, as they integrate naturally with existing services and can scale from small prototypes to enterprise-wide deployments.

Leading cloud platforms

• Microsoft Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Functions, Azure Logic Apps
• AWS Bedrock, Amazon SageMaker
• Google Vertex AI, Google AI Studio

Key advantages

• Enterprise-grade security
• Automatic scalability
• Managed AI services
• Built-in monitoring and reliability
• Easy integration with cloud resources

Things to consider

• Ongoing cloud costs
• Learning platform-specific services
• Possible dependency on a single cloud provider

Ideal use cases

• Enterprise AI applications
• Customer-facing AI services
• Large-scale automation
• AI solutions that require high availability
• Organizations already using cloud infrastructure

Summary

Cloud platforms provide a powerful foundation for AI agents by combining managed AI services with secure and scalable infrastructure. They reduce operational effort while making it easier to build production-ready AI solutions.

Open-source frameworks give developers the freedom to build AI agents without being tied to a commercial platform. They provide reusable components for common AI patterns such as agent orchestration, memory, tool calling, and workflow management, while still allowing full control over the implementation.

These frameworks evolve quickly and are often among the first to support new AI capabilities. They are a popular choice for development teams that want maximum flexibility and are comfortable managing their own infrastructure and deployments.

Popular frameworks

• LangGraph
• LangChain
• AutoGen
• CrewAI
• Semantic Kernel
• LlamaIndex

Key advantages

• Highly customizable
• No vendor lock-in
• Large developer communities
• Supports advanced agent architectures
• Easy to combine with custom code

Things to consider

• Requires software development expertise
• Community support instead of guaranteed enterprise support
• Frequent updates may introduce breaking changes
• Security and maintenance remain your responsibility

Ideal use cases

• Multi-agent systems
• Research and experimentation
• Custom AI applications
• Enterprise solutions requiring maximum flexibility
• Teams building reusable AI platforms

Summary

Open-source frameworks provide an excellent balance between flexibility and productivity. They accelerate AI development while allowing developers to keep full control over architecture and implementation.

There is no universal platform that fits every AI project. The best solution depends on your technical capabilities, business goals, existing systems, budget, and long-term strategy. In many cases, organizations combine multiple approaches—for example, using cloud services together with open-source frameworks or integrating AI into existing enterprise platforms.

Before selecting a technology, it is worth evaluating not only today's requirements but also how the solution will evolve over time.

Questions to ask

• What business problem should the AI agent solve?
• How much customization is required?
• What technical expertise does the team have?
• Which systems need to be integrated?
• What security and compliance requirements exist?
• How important are speed and long-term maintenance?

Quick comparison

A practical recommendation

Many successful AI solutions combine multiple technologies. For example:

• Use Azure OpenAI, AWS Bedrock, or Vertex AI to access AI models.
• Build business logic with LangGraph, Semantic Kernel, or AutoGen.
• Connect workflows using Power Automate, n8n, or Zapier.
• Integrate with existing enterprise platforms such as Salesforce, SAP, or ServiceNow when business data and workflows already exist.

This layered approach lets you benefit from the strengths of each technology instead of relying on a single platform.

Summary

Choose the platform that best fits your business goals, available skills, and existing technology landscape. The most successful AI projects focus on solving business problems—not on using a particular tool.