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, […]
Category: GEPA
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 […]
Introducing CodeOptiX: Universal Optimization Engine for Superior Coding Agent Experience
Today, Superagentic AI thrilled to announce CodeOptiX, a universal code optimization engine built by Superagentic AI for the new era of AI coding agents. As AI coding assistants like Claude […]
SuperOptiX Meets Pydantic AI: Optimizing MCP Tools and BaseModel Field Descriptions with GEPA
Pydantic AI has quickly become the go-to framework for building type-safe, modern AI agents. With its clean API, strong typing, and intuitive design, developers love its developer experience. But there’s […]