What is Semantic Kernel?
Semantic Kernel is Microsoft's framework for integrating LLMs with conventional programming through plugins and planners. Semantic Kernel bridges AI and traditional software engineering.
This AI developer tools and ecosystem term is currently being developed. Detailed content covering features, use cases, integration approaches, and selection criteria will be added soon. For immediate guidance on AI tooling strategy, contact Pertama Partners for advisory services.
Semantic Kernel enables .NET development teams to integrate LLM capabilities into existing enterprise applications without rewriting core business systems in Python, preserving years of accumulated codebase investment and institutional knowledge. Microsoft's active maintenance ensures compatibility with Azure OpenAI Service, reducing integration risk for organizations already committed to Microsoft cloud infrastructure and enterprise licensing agreements. mid-market companies running on Microsoft technology stacks save 4-8 weeks of integration effort compared to adapting Python-native frameworks, accelerating time-to-market for AI-enhanced features in production applications while leveraging existing developer skills and established deployment pipelines.
- Microsoft's LLM orchestration framework.
- Plugin system for tool integration.
- Planners for multi-step workflows.
- C#, Python, Java support.
- Integrates with Azure AI services.
- Alternative to LangChain with Microsoft ecosystem.
- Adopt Semantic Kernel for .NET-based enterprise environments where C# integration with existing Microsoft stack applications provides seamless plugin architecture and dependency management.
- Use the planner component to orchestrate multi-step AI workflows that chain multiple LLM calls with conventional code functions in deterministic and auditable sequences.
- Implement semantic memory stores through the built-in connector framework to add persistent context retrieval without building custom RAG infrastructure from scratch independently.
- Evaluate Semantic Kernel against LangChain based on your team's primary language proficiency since Python developers typically find LangChain's ecosystem substantially more mature.
Common Questions
Which tools are essential for AI development?
Core stack: Model hub (Hugging Face), framework (LangChain/LlamaIndex), experiment tracking (Weights & Biases/MLflow), deployment platform (depends on scale). Start simple and add tools as complexity grows.
Should we use frameworks or build custom?
Use frameworks (LangChain, LlamaIndex) for standard patterns (RAG, agents) to move faster. Build custom for novel architectures or when framework overhead outweighs benefits. Most production systems combine both.
More Questions
Consider scale, latency requirements, and team expertise. Modal/Replicate for simplicity, RunPod/Vast for cost, AWS/GCP for enterprise. Start with managed platforms, migrate to infrastructure-as-code as needs grow.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
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