A specification for agent behavior that LLM frameworks leave undefined: error handling, context management, tool execution, and state transitions.
truefoundry/trueforge
The open-source agent harness - the runtime layer that turns an LLM into a working agent.
Memory in Agents: What, Why and How
LLM memory gives language models persistent context across sessions. Learn how it works, how it differs from RAG and context windows, and how to add LLM memory to your agents with Mem0.
AI Agents: Key Concepts and How They Overcome LLM Limitations
An AI agent is an autonomous software entity that is often used to augment a large language model. Here's what developers need to know.
Agents Done Right: A Framework Vision for 2026
Agents choke on context, loop on failures, and dump walls of code for review. It's time to rethink the architecture.
Build agents, not pipelines
There are only two ways to use LLMs in a computer program: as part of a pipeline, or as an agent. In other words, either you express the control flow of the program in code, or you give a LLM tools and allow it to manage the control flow itself1.
LukeW | Agent Management Interface Patterns
As an increasing number of AI applications evolve to agents doing work for people, agent management becomes a critical part of these product's design. How can p...
Model Leaderboard | Letta
Context-Bench measures an agent's ability to perform context engineering with:
Securing the Model Context Protocol: Building a safer agentic future on Windows
As AI agents become more capable and integrated into daily workflows, the need for secure, standardized communication between tools and agents has never been greater. At Microsoft Build 2025, we’re announcing an early preview of how Windows 11 is e
Optimizing Agentic Workflows using Meta-tools
Agentic AI enables LLM to dynamically reason, plan, and interact with tools to solve complex tasks. However, agentic workflows often require many iterative reasoning steps and tool invocations,...
Harness engineering: leveraging Codex in an agent-first world
By Ryan Lopopolo, Member of the Technical Staff
Harness engineering: leveraging Codex in an agent-first world
By Ryan Lopopolo, Member of the Technical Staff
Joshua Gu on Twitter / X
Recent agentic systems (Claude Code, Codex, RLM, etc.) push context out of the prompt and into the environment (e.g., as files). This helps them maintain long-term knowledge about their goals and functionality.🚨 While this is a good idea, we show a surprising result: systems… pic.twitter.com/XHFVaDcr4l— Joshua Gu (@astrogu_) May 20, 2026
LLM Agents are simply Graph — Tutorial For Dummies
Ever wondered how AI agents actually work behind the scenes?
LLM Agents are simply Graph — Tutorial For Dummies
Ever wondered how AI agents actually work behind the scenes?