Today, we are excited to announce the open-source superoptix-lite-openai implementation of GEPA optimization with the OpenAI Agents SDK. The lite version of SuperOptiX that can be tried by anyone for […]
Category: Prompt Optimization
Agent Lightning vs SuperOptiX: Microsoft Enters the Agent Optimization Race
The Battle for Agent Optimization Supremacy Has Begun How a Superagentic AI’s pioneering vision built on top of OSS research became validated by Microsoft Research, and why the race is […]
Superagentic AI Showcased Full-Stack Agentic Optimization at ODSC AI San Francisco
Last week, Superagentic AI proudly exhibited at ODSC AI West 2025 in San Francisco, the global innovation hub of AI. We showcased our pioneering work on Full-Stack Agent Optimization, connecting […]
Introducing Forward Deployed Agents: A New Business Model for the Agentic AI Era
AI agents are everywhere now. Every conference, meetup, and organization is discussing them. Across industries, we are witnessing a once-in-a-generation shift driven by AI and agentic technologies. The field of […]
Superagentic AI Bringing Agent Optimization to ODSC AI SF: What to Expect from Our Talk and Booth
Next week, Superagentic AI is coming to ODSC West 2025 in San Francisco, our first major public appearance in the US since launching the company earlier this year. We’re travelling from London […]
Agentic Context Engineering: Prompting Strikes Back
Stanford university released paper on Agent Context Engineering (ACE) introduced structured framework to grow, refine and maintain context as living playbook that adapt itself with feedback. Everyone started talking about Context […]
Intelligent RAG Optimization with GEPA: Revolutionizing Knowledge Retrieval
The field of prompt optimization has witnessed a breakthrough with GEPA (Genetic Pareto), a novel approach that uses natural language reflection to optimize prompts for large language models. Based on the […]
GEPA DSPy Optimizer in SuperOptiX: Revolutionizing AI Agent Optimization Through Reflective Prompt Evolution
The landscape of AI agent optimization has fundamentally shifted with the introduction of GEPA as a DSPy optimizer. Unlike traditional optimization approaches that rely on trial-and-error or reinforcement learning, GEPA […]