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Introduction to Microsoft Agent Framework | Microsoft Learn Microsoft Agent Framework is an open-source development kit for building AI agents and multi-agent workflows for NET and Python It brings together and extends ideas from Semantic Kernel and AutoGen projects, combining their strengths while adding new capabilities
AI Markup: From Syntax to Execution Graphs in Agentic Workflows TL;DR: This article outlines a framework for creating a structured markup language to orchestrate AI workflows It defines a grammar for commands, entities, modifiers, and relationships, which
GitHub - agentflare-ai agentml: Agent Markdown Language AgentML is the universal language for agents, inspired by the success of HTML for the web Just as HTML lets you write content once and have it render in any browser, AgentML lets you define your agent's behavior once and run it anywhere
What is AIML? AIML is a declarative language for building AI LLM Agents using MDX and an extended version of the SCXML (State Chart XML) standard AIML makes it easy to create AI agents, simple to sophisticated, supporting complex conversation flows and code execution, tool integration, and full state management How does AIML work?
AgentML - A Deterministic Language for Agent Behavior AgentML combines the usability of XML with the predictability of state machines, giving you complete control over agent behavior XML-like syntax that's intuitive to author, diff, and audit Human-readable agent definitions that developers can understand at a glance
AIML Introduction - GeeksforGeeks AIML (Artificial Intelligence Markup Language) is a description language used in the development of natural language software agents like chatbots and virtual assistants AIML was developed by Richard Wallace from 1995 to 2000, and it is based on XML (eXtensible Markup Language)
Code execution with MCP: building more efficient AI agents Direct tool calls consume context for each definition and result Agents scale better by writing code to call tools instead Here's how it works with MCP The Model Context Protocol (MCP) is an open standard for connecting AI agents to external systems
AI Agents and the New Execution Layer in Enterprise Analytics AI agents change the model entirely They don’t just recommend, they decide, act, and adapt Instead of saying “Optimize inventory by 15%,” an agent checks confidence levels and adjusts systems on its own Here’s what these agents can do: This is how Cognitive Analytics becomes operational