AI
Much like traditional software, Agents can be built as a monolith or as a collection of microservices.
When building agents start with defining your project requirements before evaluating AI frameworks. Each framework brings unique capabilities to the table, so often the "best" is heavily dependent on what you are trying to build.
Need deep GCP integration, then choose Google ADK. Or, if you need deep AWS integration, then choose Strands Agents (from Amazon).
Another direction is to avoid crowning a single winner and instead run different agents on different frameworks. With this approach each agent + framework is treated like a microservice which allows you to optimize for specific capabilities rather than forcing everything into one framework.
The multi-framework / multi-agent approach has been proven valuable. ServiceNow embarked on a multi-agent AI collaboration by integrating NowAssist and Microsoft Copilot, orchestrated using Microsoft Semantic Kernel, to enhance P1 incident management (reference). This approach aimed for seamless collaboration across different AI platforms to coordinate complex activities and maintain context.
In addition, McKinsey has promoted the concept of an "agentic AI mesh" for integrating both custom-built and off-the-shelf agents, suggesting a multi-framework strategy.
The approach of standardizing on one AI agent framework makes sense in some situations. In other situations, a multi-framework strategy can accelerate development by leveraging the unique capabilities of each framework.
Before evaluating any framework, start with a requirements analysis. Focus on your specific technical needs, performance constraints, and business objectives for each AI agent you plan to deploy.
The LLM models available on all frameworks are the same, so that's not a deciding factor.
Start with integration needs. If your agent requires deep AWS integration, then choose Strands Agents. For GCP-heavy environments, Google ADK offers similar advantages. For onprem, Agno with it's FastAPI integration provides a solid choice. If you need OpenAI's realtime functionality, then choose OpenAI Agents SDK.
Evaluate your team's capabilities. Most frameworks are written in Python and share similar primitives, such as agents, hooks/callbacks, and guardrails. If your team has strong Python skills, switching between frameworks is relatively simple. Consider:
Understanding each framework's sweet spot transforms framework selection from guesswork to strategic decision-making.
The key insight? No framework wins everywhere. Smart companies map their agents' requirements to each framework's strengths rather than trying to find a universal solution.
The path forward isn't about finding the perfect AI agent framework. Instead, find the right fit for your application's functionality.
For enterprises ready to move beyond framework limitations, the question isn't which framework to choose, but how to orchestrate multiple frameworks effectively. This shift requires expertise in framework evaluation, architecture design, and integration patterns that maximize each platform's strengths while maintaining system coherence. The A2A Protocol is designed to enable agent-to-agent communication.
Ultimately, the choice between a single-framework vs. a multi-framework approach comes down to your unique requirements.