How do you turn a vague business style question over messy folders of CSV, JSON and text into reliable Python […]
Category: AI Agents
CMU Researchers Introduce PPP and UserVille To Train Proactive And Personalized LLM Agents
Most LLM agents are tuned to maximize task success. They resolve GitHub issues or answer deep research queries, but they […]
How to Build a Model-Native Agent That Learns Internal Planning, Memory, and Multi-Tool Reasoning Through End-to-End Reinforcement Learning
In this tutorial, we explore how an agent can internalize planning, memory, and tool use within a single neural model […]
Anyscale and NovaSky Team Releases SkyRL tx v0.1.0: Bringing Tinker Compatible Reinforcement Learning RL Engine To Local GPU Clusters
How can AI teams run Tinker style reinforcement learning on large language models using their own infrastructure with a single […]
How to Design a Persistent Memory and Personalized Agentic AI System with Decay and Self-Evaluation?
In this tutorial, we explore how to build an intelligent agent that remembers, learns, and adapts to us over time. […]
DeepAgent: A Deep Reasoning AI Agent that Performs Autonomous Thinking, Tool Discovery, and Action Execution within a Single Reasoning Process
Most agent frameworks still run a predefined Reason, Act, Observe loop, so the agent can only use the tools that […]
How to Design an Autonomous Multi-Agent Data and Infrastructure Strategy System Using Lightweight Qwen Models for Efficient Pipeline Intelligence?
In this tutorial, we build an Agentic Data and Infrastructure Strategy system using the lightweight Qwen2.5-0.5B-Instruct model for efficient execution. […]
How to Build Ethically Aligned Autonomous Agents through Value-Guided Reasoning and Self-Correcting Decision-Making Using Open-Source Models
In this tutorial, we explore how we can build an autonomous agent that aligns its actions with ethical and organizational […]
Microsoft Releases Agent Lightning: A New AI Framework that Enables Reinforcement Learning (RL)-based Training of LLMs for Any AI Agent
How do you convert real agent traces into reinforcement learning RL transitions to improve policy LLMs without changing your existing […]
How Exploration Agents like Q-Learning, UCB, and MCTS Collaboratively Learn Intelligent Problem-Solving Strategies in Dynamic Grid Environments
In this tutorial, we explore how exploration strategies shape intelligent decision-making through agent-based problem solving. We build and train three […]