AI Agent Frameworks Matter
AI agents automate complex, multi-step workflows that require manual, error-prone work, like data entry, conducting research and processing tickets. They seem simple in theory, just software calling an AI model, but building agents without taking governance and security into account from the ground up can create more problems than they solve. That’s why choosing the appropiate AI Agent Framework is essential.
The Hidden Complexity of Production Agents
Building production grade agents requires a lot more than just integrating with your favourite LLM provider’s API. These include:
- Multi-agent support and workflow orchestration patterns.
- State management to maintain context across multi-turn interactions.
- Observability for monitoring, logging, and debugging agent behavior.
- Human-in-the-loop mechanisms for approval workflows and escalation.
- Tool integrations with enterprise systems.
- Evaluation tools that ensure your agents behave consistently and are protected against common attacks.
All of this amounts to months of engineering before delivering any meaningful business value.
What AI Agent Frameworks Provide
Frameworks built for enterprise use deliver:
- Standardization and best practices – proven patterns for agent design reducing architectural decisions and enabling consistency across teams.
- Built-in enterprise-grade features like observability and traceability, and governance features such as human-in-the-loop mechanisms.
- High-level abstractions and built-in features that allow developers to ship in weeks instead of months.
- Reduced risk of technical debt through ongoing maintenance and security patches handled by the framework maintainers.
These capabilities directly address some of the biggest stumbling blocks to agent adoption. Observability means you can trace every decision an agent makes, catching errors before they reach customers. Human-in-the-loop mechanisms let you require approval for high-stakes actions.
Google ADK vs Microsoft Agent Framework
This year, we’ve seen two big players take a similar, yet major step in the ai agent framework space. In April, Google released the Agent Development Kit, and on October 1st, Microsoft announced their entry: The Microsoft Agent Framework.
Both of these take a similar approach: They offer a high level of abstraction and a built-in development UI to enable you to launch your first prototype in minutes, and immediately begin evaluating it with an intuitive user interface. Both prioritize enterprise features, deployment flexibility, and ecosystem integration. They also integrate with OpenTelemetry, providing robust observability, allowing you to easily integrate with your existing monitoring infrastructure.
While the frameworks handle orchestration and workflow management, the real security and governance capabilities come from their underlying platforms. Azure AI Foundry provides content safety filters, prompt shields, and compliance logging. Google Vertex AI offers similar protections with grounding checks and safety attributes.
Google Agent Development Kit and Microsoft Agent Framework are new players in the field, so major adoption is yet to be seen, but since both focus on enterprise capabilities and ecosystem integration, development teams already leveraging Microsoft’s and Google’s platforms should take note and seriously consider the benefits these frameworks provide.
Microsoft AF is still in preview, but it is a merger of two existing, already established frameworks – AutoGen and Semantic Kernel. Google ADK, on the other hand, has been released, but it’s a completely new product.
Choose Google ADK if your team already uses Google Cloud and Vertex AI, or if you prefer Java over Python. Choose Microsoft Agent Framework if you’re on Azure, migrating from AutoGen or Semantic Kernel, or if your team works primarily in C#.
Both tie you to their cloud ecosystems, but if you’re already committed to Azure or GCP, that’s a feature, not a bug.
Our Take
At Cloudamite, we’ve experimented with both of these frameworks, and so far, we’re impressed by how quickly you can get a prototype up and running: thanks to their design, both frameworks get you there in minutes.
If you’re in a stage where you’re still exploring where AI agents could add value to your workflows, I highly encourage you to experiment with these ai agent frameworks. Pick one and build a proof-of-concept today:
P.S they’re both Open Source, so be sure to check out our blog post on why Your Company Needs an Open Source Strategy
