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.

Anthropic Commits $150M to Put Claude in 15+ US Federal Agencies
Anthropic will spend $150M over three years on the Genesis Mission, giving NASA, NIH, NSF and a dozen-plus other agencies access to Claude. Fusion energy and…

Five-Layer Immutable Memory for an AI Agent: Why One Vector Database Is Never Enough
Trying to store an AI agent's entire long-term memory in a single vector index fails, because exact word search, semantic similarity, multi-hop relationship…

PatEx-Gym Teaches AI Agents to Hunt for Patent Prior Art: 1 Million Patents and a 4B Model
Researchers have released PatEx-Gym, an environment for patent-examiner agents: 1,000 tasks, 1 million patents, 52,000 papers, and a compact 4-billion-parameter…

The Information: Anthropic Is Preparing a Model Stronger Than Any Current Claude

Tencent Hunyuan and Fudan Launch ExplorationBench to Measure Exploration in LLMs

WorldMemBench Tests Whether World Models Remember Rooms and Events

LightSprint Raises $6.1M for a Multi-Agent Product-to-Engineering Loop

Cohere proposes a fix for agent sprawl: competitive selection instead of bans
The proliferation of AI agents inside companies is a management problem. Cohere proposes pitting competing agents against each other on reliability, cost, and speed…

Vercel Launches Python in General Availability: Six SDKs and FastAPI Support
Vercel launched Python in general availability, enabling developers to deploy Python applications alongside JavaScript frontends with automatic framework detection…

CodeActionBench: GPT-6 Astra in Codex CLI Hits 73.3% Success on Two-Handed Tasks

Apple and CMU Speed Up AI Agents 10x by Replacing Reasoning with Selection

The AI development paradox: why greenfield coding speeds up 55% while mature codebases slow down 19%

ProD Stops AI Agents From Looping: Tested on 8 Models (0.6B–8B)

Alibaba DAMO Releases RynnUnify: A 16B Model That Plans in Video and Drives a Robot
The 16B-parameter RynnUnify combines scene understanding, video generation and robot control: 51.5% success on 20 RoboCasa365 tasks and 46.7% on three real-world…

RSIHub Packs 4 Agent Evolution Methods Into One Framework: Up to +13.5 pp
The open-source RSIHub provides shared infrastructure for comparing methods of AI agent self-improvement. The largest pass@1 gain in testing was 13.5 percentage…









