Semantic Kernel
PulseAugur coverage of Semantic Kernel — every cluster mentioning Semantic Kernel across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
-
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…
-
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, …
-
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…
-
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 …
-
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…
-
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 …
-
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…
-
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…
-
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…
-
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 …
-
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,…
-
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, …
-
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…
-
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…
-
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…
-
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…
-
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…
-
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…
-
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…
-
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…