Semantic Kernel
PulseAugur coverage of Semantic Kernel — every cluster mentioning Semantic Kernel across labs, papers, and developer communities, ranked by signal.
7 day(s) with sentiment data
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AI agent hype on LinkedIn masks reality; focus on core patterns, not frameworks
The author argues that the current hype around AI agents, particularly on platforms like LinkedIn, is misleading. Many systems labeled as 'agents' are merely complex function calls or chat interfaces, lacking true objec…
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AI agents: Over-engineering and confusion plague current deployments
The current definition and widespread use of the term "AI agent" are causing engineering confusion, leading to over-engineering simple tasks and under-engineering complex ones. A true agent should possess an objective, …
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AI Agents: Industry insiders reveal the reality behind the hype
The current rush to label systems as "AI agents" is causing confusion and engineering missteps, according to industry insiders. A true agent, defined as a system with an objective that can decide its next steps, handle …
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Microsoft Foundry streamlines LLMOps with updated Azure AI tools
Microsoft has updated its LLMOps framework, now branded as Microsoft Foundry, to streamline the deployment and management of large language models in production environments. The updated system integrates components lik…
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Developer shares festive AI tutorial using Semantic Kernel
A developer is participating in the Festive Tech Calendar event by sharing AI-related content. The developer's contribution focuses on a "naughty or nice checker" tool, likely built using .NET and Semantic Kernel, and i…
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AI agents: Over-hyped, narrowly deployed, focus on fundamentals
The current discourse around AI agents is overly broad, leading to engineering missteps where simple workflows are over-engineered and complex ones are under-engineered. A true agent, as opposed to a mere function call …
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AI agents move from text generation to tool use and structural co-design
A new paper explores using AI agents to co-design structural elements in the AEC industry, connecting natural language prompts to structural modeling APIs. Separately, a developer guide details how to implement function…
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AI Agents in .NET, Local Prompt Engines, and Model Performance Compared
A technical guide explores building AI agents in .NET, detailing architecture, tool and function calling, planning, memory, autonomy, multi-agent systems, and security considerations like prompt injection. The guide emp…
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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…