AI Agents
AI agents are LLM-powered systems that don't just answer — they plan steps, call tools and drive a task to completion: writing code, searching the web, booking, analyzing. 2026 became the year of agents, from coding agents to autonomous researchers. This page gathers all our coverage of agentic AI: launches, protocols (MCP, A2A), reliability and real-world use.
Spring Agent Toolkit: effective tools for managing AI agents
AI agents are rapidly moving from the category of experiments into production systems, where every penny spent on tokens matters.
OpenAI launched /goal in Codex CLI: autonomous agent or an expensive way to code?
Codex CLI got /goal — an autonomous development mode. In practice: token usage spikes 5x, MCP integrations fail silently, and the cost of this "automation" stays…
Memory for an AI Agent: From Complexity to 300 Lines of Code
A developer described the search for a working memory system for AI agents — from the complex MemPalace architecture to a simple markdown-based solution and…
How to Measure an AI Agent's Performance in QA: The Story of a Benchmark
Two windows with an AI agent instead of one: architect, developer, and six types of errors
MELT-1: how Metabolic AI tests agents for survival
5 AI agents wrote an article about 5 AI agents: how to scale content
Skills for AI agents: why they conflict with each other
When new skills are added to AI agents, unexpected conflicts often arise: one triggers all the time, another is ignored, and a third interferes with neighboring…
Hermes and OpenClaw: philosophy vs. functionality in AI agents
Two open frameworks for AI agents look similar in functionality, but stem from opposite design philosophies.
Intruder created an AI agent that conducts pentests in minutes instead of days
LazyWeb connects AI agents to the designs of 257,000 real apps
Alibaba integrated Qwen AI into Taobao and Tmall for agentic shopping
Which code architectures suit AI agents best
How Memori creates persistent memory for agents and multi-session LLMs
Memori is a memory infrastructure for LLM applications that lets agents retain context across sessions and work with multiple users at the same time.
Bain estimates SaaS agentic AI market at $100 billion
Bain & Company forecasts that the U.S. market for SaaS applications built on agentic AI will grow to $100 billion, driven by the automation of office workflows.