OpenAI released a major evolution of its Agents SDK as a fundamental rethinking of how agents should operate in production. They introduced an open, inspectable harness for orchestration and a clean […]
Official Meta-Harness Repo + Packaged Power = Coding Agent metaharness
Stanford IRIS Lab officially released the reference code for Meta-Harness, their groundbreaking framework for autonomously optimizing the code scaffolding around a fixed large language model. The announcement quickly gained traction […]
Open Memory and Open Harness Is Not Enough: You Need Self-Optimizing (Self-Healing) Harness
Recently there is a lot of discussion on agent Main and harness initially anthropic put a blog post on scaling managed agents which gone viral is and replied to that […]
What OpenClaw Vs Anthropic Drama Taught Us: The Urgent Need for Self-Optimizing Harness Engineering
Recently, OpenClaw took off like a one of the greatest breakthrough in the AI. People are going crazy to setup OpenClaw to automate the tasks. All looked very good until […]
Gemma 4 with MLX for Local Agentic AI at Superagentic AI
At Superagentic AI, we have published a new MLX 4-bit conversion of Gemma 4 31B IT for Apple Silicon workflows. The model is now available on Hugging Face at SuperagenticAI/gemma-4-31b-it-4bit-mlx. […]
Meta-Harness: A Self-Optimizing Harness Around Coding Agents
Stanford AI lab just released the Meta Harness paper which covers the meta harness strategy that self-optimise. Most conversations about coding agents focus on the model. People compare model quality, […]
Harness Engineering: Why It’s Suddenly the Hottest Topic in AI Agent Engineering
If you build agents, you already know the feeling: the model is smarter than ever, yet your agent still flakes on long tasks, loses context, or ships brittle code. The […]
Turbocharge Pydantic AI + SurrealDB RAG with TurboAgents and TurboQuant
Google Research released the TurboQuant, the game changing compression technique also Superagentic AI released the TurboAgents to showcase the use of the TurboQuant in the real Agentic AI systems. This post […]
Introducing TurboAgents: Supercharge Your Agents with TurboQuant
Google research released the TurboQuant the technique that compress the context for LLMs. The next bottleneck in agent systems is not just model quality. It is retrieval cost, memory pressure, […]
Introducing Agentnetes: Self-Discovering AI Agent Swarms, On Demand
On 21 March, I attended Zero to Agent London, a hackathon hosted by Google DeepMind and Vercel. The challenge was simple: build something with agents. The result was Agentnetes, an […]