Microsoft shipped Agent Framework 1.0 on April 7.
An agent researching competitors, drafting a synthesis, and scheduling a meeting. Fourteen steps in, the container gets rescheduled.
We rolled out follower notifications a couple of weeks ago. Writers publish a post, their followers get a ping in the feed.
Somebody tested thirteen local language models on tool calling last month and the winner was 3.4 gigabytes.
Twelve months ago, most agent teams cared about one protocol: MCP. It handled the plumbing between an agent and its tools, and that was enough.
Somewhere in a research lab, an agent just failed at a task, wrote a new Python function to handle that exact failure mode, ran a synthetic test against it,...
Microsoft just shipped the Release Candidate for Agent Framework 1.0, and in the process killed both AutoGen and Semantic Kernel.
Paperclip hit 42,000 GitHub stars in a month. The pitch: model your multi-agent system as a company.
Most agent loops work like this: the model picks a tool, calls it, gets the result, picks the next tool. Rinse, repeat.
A 3.4 GB model just posted a 97.
Google dropped Gemma 4 on Wednesday — four open-weight models under a genuine Apache 2.0 license, built from the same research behind Gemini 3.
Over the past three months, OpenAI retired Swarm and shipped the Agents SDK with first-class handoffs.
NVIDIA dropped Nemotron 3 Super a few weeks ago and it flew under the radar — buried by the Mythos leak drama and GPT-5.4's benchmark parade.
While everyone was busy arguing about GPT-5.
Everybody wants a multi-agent system.
A year ago, people were debating whether MCP would become the standard for connecting LLMs to tools. That debate is over — MCP won.
If you've been training or fine-tuning large models, you've probably hit that moment — loss curve looks beautiful for hours, then suddenly spikes into...