Semantic Kernel
What is Semantic Kernel?
Semantic Kernel is a developer framework for building AI agents that turn prompts and existing APIs into function calls. It sits between the model and your code, using plugins and connectors to orchestrate workflows, with support for C#, Python, and Java plus observability, security, and filters. It integrates with GitHub, Logic Apps, and Azure Container Apps Dynamic Sessions.
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At a glance
- Semantic Kernel is best for developers who need to connect AI models to existing code and workflows.
- Yes — Semantic Kernel combines prompts with existing APIs and plugins to translate model requests into function calls.
What does Semantic Kernel do?
Semantic Kernel turns prompts and existing APIs into function calls so developers can build AI agents that act on real systems instead of only generating text. It sits between the model and your code, translating requests into calls through plugins and connectors, then passing results back into the model loop. The docs show modular extensibility, with out-of-the-box connectors and support for C#, Python, and Java, plus enterprise components such as observability, security, and filters. At scale, Microsoft says the framework is already used by Microsoft and other Fortune 500 companies, and that version 1.0+ support across C#, Python, and Java is designed to avoid breaking changes. It is built to stay future-proof as new models and modalities arrive, so teams can swap models without rewriting their codebase. The documentation also points to GitHub, Logic Apps, and Azure Container Apps Dynamic Sessions as integration paths for extending agents and workflows.
Why use Semantic Kernel?
- Its middleware approach lets teams reuse existing APIs and code instead of rebuilding business logic around the model.
- Microsoft positions it as future-proof, so new models can be adopted by swapping components rather than rewriting applications.
Who is Semantic Kernel for?
- Application developers who want AI agents to call existing functions and services.
- Platform teams who need modular AI components that fit into current codebases.
- Enterprise architects who need observable, security-aware AI systems at scale.
- Python, C#, and Java teams who want one framework across multiple languages.
- Automation builders who need AI to trigger workflows and business processes.
What are Semantic Kernel's key features?
Getting Started
Guides first-time builders through Semantic Kernel setup and sample apps, helping teams move from docs to a working AI app faster with Microsoft Learn examples.
Quick Start
Provides a short path to running prompts with existing APIs and plugins, so developers can validate function-calling workflows without building everything from scratch.
Concepts
Explains core building blocks for prompts, APIs, and plugins, giving teams the vocabulary needed to design maintainable AI workflows and avoid brittle implementations.
Frameworks
Shows how Semantic Kernel fits into application frameworks and model-driven code, helping teams structure AI features around reusable components instead of one-off scripts.
Enterprise Components
Covers enterprise-oriented building blocks for production AI systems, including API-based function calls and plugin orchestration that support controlled integration with business systems.
Enterprise ready
Documents production-focused guidance for building AI solutions that can evolve with changing models and APIs, reducing rework as the platform and tooling change.
Automating business processes
Uses prompts plus existing APIs and plugins to translate model requests into function calls, making it easier to automate workflows across systems like Logic Apps.
Modular and extensible
Supports modular AI app design with plugins and extensible components, so teams can add capabilities incrementally and connect services such as GitHub or Azure Container Apps Dynamic Sessions.
What does Semantic Kernel integrate with?
- GitHub
- Logic Apps
- Azure Container Apps Dynamic Sessions
What are Semantic Kernel's use cases?
Agent workflows for developers
Application developers use Semantic Kernel to let AI agents call existing functions and services inside their apps, using Quick Start to get a working prototype fast. They then use Concepts and Frameworks to wire prompts, plugins, and code into a reliable workflow that can trigger real actions instead of just generating text.
Enterprise AI architecture
Enterprise architects use Semantic Kernel to build observable, security-aware AI systems that fit scale requirements, using Enterprise Components to standardize how agents connect to business logic. Enterprise ready helps them keep deployments aligned with governance and operational needs across larger systems.
Cross-language team adoption
Python, C#, and Java teams use Semantic Kernel to share one AI framework across different codebases, leaning on Frameworks and Modular and extensible to keep implementations consistent. That makes it easier to reuse patterns, reduce duplicated agent logic, and move faster across services.
Workflow automation for ops
Automation builders use Semantic Kernel to trigger workflows and business processes from AI prompts, using Automating business processes to connect model output to real operational steps. They can pair that with GitHub or Logic Apps to move from suggestion to execution with less manual handoff.
How does Semantic Kernel work?
- Start with Quick Start to connect your first app or service and see Semantic Kernel translate model requests into function calls. Use Get started if you want the shortest path to a working first integration.
- Explore Concepts to understand prompts, plugins, and how the framework organizes AI behavior. This gives your team a shared vocabulary before you wire agents into production code.
- Choose Frameworks and Modular and extensible to fit Semantic Kernel into your existing codebase. Add only the components you need, then extend them as your workflows grow.
- Use Enterprise Components and Enterprise ready to shape security-aware, observable deployments. Align the setup with operational controls so AI actions stay manageable at scale.
- Connect Automating business processes with GitHub, Logic Apps, or Azure Container Apps Dynamic Sessions to trigger real workflows. Keep iterating from there as your agents take on more tasks.
Frequently asked questions
What is Semantic Kernel?
Semantic Kernel is a developer framework for building AI agents that turn prompts and existing APIs into function calls. It sits between the model and your code, using plugins and connectors to orchestrate workflows, with support for C#, Python, and Java plus observability, security, and filters. It integrates with GitHub, Logic Apps, and Azure Container Apps Dynamic Sessions.
What is Semantic Kernel used for? Who is it for?
Semantic Kernel is used for Getting Started, Quick Start, and Concepts. It's built for Application developers, Platform teams, and Enterprise architects.
Does Semantic Kernel have an API and what does it integrate with?
Semantic Kernel combines prompts with existing APIs and plugins to translate model requests into function calls. It integrates with GitHub, Logic Apps, Azure Container Apps Dynamic Sessions.
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