Semantic Kernel
PulseAugur coverage of Semantic Kernel — every cluster mentioning Semantic Kernel across labs, papers, and developer communities, ranked by signal.
10 day(s) with sentiment data
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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,…
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AI agents: hype vs. reality in production deployments
The author argues that the current hype around AI agents is misleading, as many systems labeled as agents are merely sophisticated function calls. True agents, in the author's view, possess objectives, handle failures, …
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Run Semantic Kernel Apps Locally for Free
This article discusses how to run applications built with Semantic Kernel locally and for free, avoiding cloud computing costs. It provides a guide for setting up these applications to operate on a local machine, making…
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AI Apps May Forget Conversations With Simple Prompts
AI applications may be losing conversational context if users employ overly simplistic prompts. This issue arises because basic prompts may not provide enough information for the AI to maintain a coherent and extended d…
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AI Agents: Overhyped Demos vs. Production Reality
The author argues that the term "AI agent" is being overused, leading to engineering mistakes. A true agent, they contend, has an objective and can decide its next steps, handle failures, and know when it's done, unlike…
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AI agents need clear objectives, not just fancy prompts
The author argues that the current hype around AI agents is diluting the term, leading to engineering mistakes. A true agent, they contend, must have an objective and decide its own next steps, rather than merely execut…
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Enterprise LLM integration fails due to lack of observability and cost control
An enterprise .NET team experienced significant issues after integrating Azure OpenAI directly into their production application. The primary problems encountered were a lack of observability, leading to difficulties in…
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Microsoft launches open-source standard for AI agent control
Microsoft has released an open-source standard called the Agent Control Specification (ACS) to help developers manage the behavior of AI agents. ACS allows teams to define policies that dictate what agents can and canno…
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Developer clarifies RAG, Function Calling, MCP, and Semantic Kernel
This article details a developer's journey in understanding and applying Retrieval-Augmented Generation (RAG), Function Calling, MCP, and Semantic Kernel within .NET AI applications. It aims to clarify when to utilize e…
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AI coding scaffold 'superpowers' surges on GitHub with 200k stars
A new AI coding scaffold called superpowers has gained significant traction on GitHub, attracting nearly 200,000 stars. This tool aims to bridge the gap between raw AI models and professional software development by inj…
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AI agents can now accept Lightning Network payments
A new set of open-source middleware packages has been released to integrate Lightning Network payments into AI agent frameworks. These packages, available on npm, allow developers to gate access to AI tools and services…
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AI agents gain communication ability with new A2A protocol
The Agent2Agent (A2A) protocol aims to solve the challenge of enabling multiple AI agents to communicate and collaborate effectively. Initially, teams often resort to duplicating agent systems for each new client, leadi…
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AI agent frameworks pose systemic execution risks via prompt injection
AI agents equipped with plugins introduce new execution risks beyond traditional content vulnerabilities. Prompt injection can now lead agents to perform unintended actions by manipulating parameters passed to tools. Fr…
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AI agents evolve from single prompts to coordinated workforces
The development of AI is shifting from single, monolithic prompts to coordinated multi-agent systems, which offer improved performance by decomposing complex tasks. Each agent in these systems has a specialized role, le…
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AI agent frameworks face RCE vulnerabilities from prompt injection attacks
Security researchers have identified critical remote code execution (RCE) vulnerabilities within several popular AI agent frameworks. These flaws stem from improper handling of user-supplied prompts, which can be manipu…