Semantic Kernel
PulseAugur coverage of Semantic Kernel — every cluster mentioning Semantic Kernel across labs, papers, and developer communities, ranked by signal.
5 day(s) with sentiment data
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Agentic RAG vs Traditional RAG in .NET: Implementation and Metrics
This article explores the differences between Agentic RAG and Traditional RAG within the .NET framework, detailing scenarios where each approach excels. It delves into the implementation of Semantic Kernel code and disc…
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AI agent definitions are misleading; production systems focus on narrow tasks and robust design
The current definition and application of "AI agents" are often misleading, with many systems labeled as agents merely performing complex function calls rather than exhibiting true objective-driven behavior. In producti…
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LLM context costs challenge .NET developers; optimization strategies outlined
For .NET developers using Azure OpenAI, managing the cost and latency associated with large context windows is crucial. The quadratic scaling of attention mechanisms means that doubling prompt length can quadruple expen…
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ASP.NET Core LLM Apps Need Trace-First Observability for Cost and Latency
Observability for LLM applications in ASP.NET Core requires a trace-first approach to manage costs, latency, and model quality. A single misconfigured prompt can lead to significant cost increases and performance issues…
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AI Agents: Production Reality vs. Hype, Focus on Core Patterns
Developers are finding that the current hype around AI agents is often misapplied, leading to engineering mistakes. True agents possess objectives and decision-making capabilities, unlike simple function calls or chat i…
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AI agents: Production reality vs. hype, complexity is the real challenge
The current discourse around AI agents is overly broad, with many systems being labeled as agents when they are merely advanced function calls. True agents possess objectives, make independent decisions, handle failures…
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New .NET Checklist Addresses OWASP LLM Security Gaps
A developer has created a .NET/C# implementation checklist for the 2026 OWASP Top 10 LLM Applications list because existing resources were primarily in JavaScript or TypeScript. The new checklist provides specific code …
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AI Engineers Warn of Production Gap, Define 'Agent' Critically
Many AI engineers are concerned about the gap between AI demonstrations and real-world production systems, particularly regarding the definition and application of "agents." An agent is precisely defined as a system wit…
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AI agent definitions diluted; production focus shifts to core patterns
The current discourse around AI agents is overly broad, with many systems being mislabeled as agents when they are merely sophisticated function calls. True agents possess objectives, handle failures, and know when they…
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AI Frameworks: Evaluating Core Functions Beyond Provider Abstraction
AI frameworks like LangChain, LlamaIndex, and CrewAI offer four core functionalities: provider abstraction, control flow, integrations, and operations. While many teams only need one or two of these features, they often…
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AI agents: Over-engineering and definition dilution plague production systems
The author argues that the current hype around AI agents is diluting their definition, leading to engineering mistakes. True agents, unlike simple function calls or chat interfaces, possess objectives, handle failures, …
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AI agents overhyped; focus on core patterns, not frameworks
The author argues that the current hype around AI agents is diluting the term and leading to engineering mistakes. True agents, defined as systems with objectives that can decide their next steps and handle failures, ar…
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Microsoft Agent Framework routes messages by Python type, uses Pregel engine
Microsoft has released version 1.12.0 of its Agent Framework, a project that merges AutoGen and Semantic Kernel. This framework routes messages between agents based on Python types, ensuring that messages are delivered …
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AI agents: Production reality vs. inflated hype · 1 source tracked
The current discourse around AI agents is often inflated, with many systems being mislabeled as agents when they are merely advanced function calls. True agents possess objectives, make independent decisions, handle fai…
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AI agents are often mislabeled; focus on tool design, not just models
The current discourse around AI agents is overly broad, with many systems being mislabeled as agents when they are merely sophisticated function calls. True agents possess objectives, make independent decisions, handle …
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AI-generated codebases show 92% vulnerability rate, researchers find
A recent security audit revealed that 92% of AI-generated codebases contain critical vulnerabilities, with an average of 8.3 exploitable findings per application. This alarming trend was highlighted by Microsoft's Secur…
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AI agents: Hype vs. reality in production deployments
The author argues that the current hype around AI agents is misleading, with many systems labeled as agents actually being simple function calls or chat interfaces. True agents, according to the author, possess an objec…
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AI agents: Defining true capability beyond fancy function calls
The current definition and widespread use of "AI agents" are causing engineering mistakes due to a lack of precise definition. A true agent should have an objective, decide its next steps, handle failures, and know when…
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Microsoft releases Semantic Kernel SDK for LLM integration
Microsoft has released Semantic Kernel, an open-source SDK designed to integrate large language models (LLMs) with existing code and APIs. Available in C#, Python, and Java, it acts as a bridge between applications and …
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AI Agents: Focus on Architecture, Not Hype, Says Expert
The author argues that the current hype around AI agents is misleading, with many systems being mislabeled as agents when they are merely complex function calls. True agents, according to the author, possess objectives,…