The coding-model landscape is moving faster than the software around it. In the space of a few weeks, OpenAI introduced GPT-5.6, xAI released Grok 4.5, Meta launched Muse Spark 1.1, […]
Category: Harness Engineering
Self-Optimizing Coding Agent Harnesses with SuperQode
Coding agents have become a practical part of software development workflows. Tools such as Codex-style agents, Claude Code-style agents, OpenCode-style agents, and IDE-integrated assistants are increasingly good at reading repositories, […]
Optimizing Databricks Omnigent Agents with MetaHarness
Databricks has just released the Omnigent the meta-harness for the AI agents. It is timely that Superagentic AI released meta-harness library few months ago. These meta-harness concepts sounds same but they […]
Superagentic AI at the Google I/O and CAIS Conference: Reflections from Bay Area
Last week, I returned to London from the San Francisco Bay Area with renewed energy, fresh insights, and a deep appreciation for the people driving artificial intelligence forward. Since back, […]
PyFlue 0.2.0: Bringing Flue’s Agent Runtime Model to Python
PyFlue 0.2.0 is now available. This release is a major step toward parity with the TypeScript Flue framework and introduces a clearer runtime model for building production-oriented Python agents. The […]
Introducing PyFlue: The Python-Native Agent Harness Framework Inspired by Flue.
The CEO of HTML, Fred Schott released Flue , the TypeScript community quickly recognized its significance. A true agent harness framework with Markdown-driven skills, headless and programmable design, zero-config sandboxing, and […]