New agent framework matches human-engineered AI systems

Agents built on top of today's models often break with simple changes — a new library, a workflow modification — and require a human engineer to fix it. That's one of the most persistent challenges in deploying AI for the enterprise: creating agents that can adapt to dynamic environments without constant hand-holding. While today's models are powerful, they are largely static.

To address this, researchers at the University of California, Santa Barbara have developed Group-Evolving Agents (GEA), a new framework that enables groups of AI agents to evolve together, sharing experiences and reusing their innovations to autonomously improve over time.

Anthropic's Sonnet 4.6 matches flagship AI performance at
one-fifth the cost, accelerating enterprise adoption

Anthropic on Tuesday released Claude Sonnet 4.6, a model that amounts to a seismic repricing event for the AI industry. It delivers near-flagship intelligence at mid-tier cost, and it lands squarely in the middle of an unprecedented corporate rush to deploy AI agents and automated coding tools.

OpenAI deploys Cerebras chips for 'near-instant' code generation in first major move beyond Nvidia

OpenAI on Thursday launched GPT-5.3-Codex-Spark, a stripped-down coding model engineered for near-instantaneous response times, marking the company's first significant inference partnership outside its traditional Nvidia-dominated infrastructure. The model runs on hardware from Cerebras Systems, a Sunnyvale-based chipmaker whose wafer-scale processors specialize in low-latency AI workloads.

The Rise of WebMCP

In this video, I look at Web MCP and how it could have a huge impact on how agents operate on the web and work with websites.

MiniMax's new open M2.5 and M2.5 Lightning near state-of-the-art while costing 1/20th of Claude Opus 4.6

Chinese AI startup MiniMax, headquartered in Shanghai, has sent shockwaves through the AI industry today with the release of its new M2.5 language model in two variants, which promise to make high-end artificial intelligence so cheap you might stop worrying about the bill entirely. 

It's also said to be "open source," though the weights (settings) and code haven't been posted yet, nor has the exact license type or terms. But that's almost beside the point given how cheap MiniMax is serving it through its API and those of partners.

z.ai's open source GLM-5 achieves record low hallucination rate and leverages new RL 'slime' technique

Chinese AI startup Zhupai aka z.ai is back this week with an eye-popping new frontier large language model: GLM-5.

The latest in z.ai's ongoing and continually impressive GLM series, it retains an open source MIT License — perfect for enterprise deployment – and, in one of several notable achievements, achieves a record-low hallucination rate on the independent Artificial Analysis Intelligence Index v4.0. 

With a score of -1 on the AA-Omniscience Index—representing a massive 35-point improvement over its predecessor—GLM-5 now leads the entire AI industry, including U.S. competitors like Google, OpenAI and Anthropic, in knowledge reliability by knowing when to abstain rather than fabricate information.

'Observational memory' cuts AI agent costs 10x and outscores
RAG on long-context benchmarks

RAG isn't always fast enough or intelligent enough for modern agentic AI workflows. As teams move from short-lived chatbots to long-running, tool-heavy agents embedded in production systems, those limitations are becoming harder to work around.

In response, teams are experimenting with alternative memory architectures — sometimes called contextual memory or agentic memory — that prioritize persistence and stability over dynamic retrieval.

One of the more recent implementations of this approach is "observational memory," an open-source technology developed by Mastra, which was founded by the engineers who previously built and sold the Gatsby framework to Netlify.