The team behind GEPA has just released the Omni to make agent optimisation unified. SuperQode, our agent engineering platform for coding factories has already integrated this and experienced with tit for […]
Category: GEPA
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, […]
Year One of Superagentic AI: From Apple to Agentic AI Engineering
Today, April 28 2026, marks the first anniversary of Superagentic AI. One year ago, on April 28 2025, Superagentic AI was officially incorporated. A few days before that, I had […]
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, […]
CodexOpt: Optimize AGENTS.md and SKILL.md for Codex with GEPA-Inspired Feedback
Modern coding agents are getting better fast. But for most teams, one problem remains stubbornly manual: the instructions that shape agent behavior. A repo might have an AGENTS.md. It might […]
How Many Types of Agent Engineering Exist Right Now?
The AI industry has started producing a new engineering label almost every month. Prompt Engineering. Context Engineering. Harness Engineering. Eval Engineering. Memory Engineering. Skills Engineering. Guardrail Engineering. Inference Engineering. And […]
Introducing Super Code Mode: Optimize Code Mode with GEPA. Run Anywhere.
There is a real shift happening in how teams build AI agents on top of MCP (Model Context Protocol). For a while, the default pattern was simple: expose lots of […]
Superagentic AI Open-Sources SuperOptiX Agent Optimization Engine
Superagentic AI is open sourcing SuperOptiX. This is a major milestone in our journey and a practical step for teams building production agentic systems in a fast-moving ecosystem. Where SuperOptiX […]
Introducing SuperOpt: Research on Agentic Environment Optimization for Autonomous AI Agents
We are excited to announce the public release of SuperOpt, a groundbreaking research framework that redefines how we optimize autonomous AI agents. Instead of retraining massive language models, SuperOpt optimizes […]