The landscape of AI is expanding. Today, many of the most powerful LLMs (large language models) reside primarily in the […]
Category: agentic AI
Meet LangChain’s DeepAgents Library and a Practical Example to See How DeepAgents Actually Work in Action
While a basic Large Language Model (LLM) agent—one that repeatedly calls external tools—is easy to create, these agents often struggle […]
A Guide for Effective Context Engineering for AI Agents
Anthropic recently released a guide on effective Context Engineering for AI Agents — a reminder that context is a critical […]
An Implementation to Build Dynamic AI Systems with the Model Context Protocol (MCP) for Real-Time Resource and Tool Integration
In this tutorial, we explore the Advanced Model Context Protocol (MCP) and demonstrate how to use it to address one […]
Weak-for-Strong (W4S): A Novel Reinforcement Learning Algorithm that Trains a weak Meta Agent to Design Agentic Workflows with Stronger LLMs
Researchers from Stanford, EPFL, and UNC introduce Weak-for-Strong Harnessing, W4S, a new Reinforcement Learning RL framework that trains a small […]
Kong Releases Volcano: A TypeScript, MCP-native SDK for Building Production Ready AI Agents with LLM Reasoning and Real-World actions
Kong has open-sourced Volcano, a TypeScript SDK that composes multi-step agent workflows across multiple LLM providers with native Model Context […]
Qualifire AI Releases Rogue: An End-to-End Agentic AI Testing Framework, Evaluating the Performance of AI Agents
Agentic systems are stochastic, context-dependent, and policy-bounded. Conventional QA—unit tests, static prompts, or scalar “LLM-as-a-judge” scores—fails to expose multi-turn vulnerabilities […]
A Coding Guide to Build an AI-Powered Cryptographic Agent System with Hybrid Encryption, Digital Signatures, and Adaptive Security Intelligence
In this tutorial, we build an AI-powered cryptographic agent system that combines the strength of classical encryption with adaptive intelligence. […]
Qualifire AI Open-Sources Rogue: An End-to-End Agentic AI Testing Framework Designed to Evaluate the Performance, Compliance, and Reliability of AI Agents
Agentic systems are stochastic, context-dependent, and policy-bounded. Conventional QA—unit tests, static prompts, or scalar “LLM-as-a-judge” scores—fails to expose multi-turn vulnerabilities […]
Building a Context-Folding LLM Agent for Long-Horizon Reasoning with Memory Compression and Tool Use
In this tutorial, we explore how to build a Context-Folding LLM Agent that efficiently solves long, complex tasks by intelligently […]