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 […]
Tag: Agent Engineering
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 […]
Introducing SuperRadar: The Global Intelligence Radar to Stay Competitive in the Agentic AI Race
Artificial Intelligence is moving faster than any technology wave in recent memory. Every week, new Agentic frameworks, orchestration libraries, coding assistants, and evaluation platforms appear, each claiming to be revolutionary. […]
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 […]
Superagentic AI Joins UK AI Trade Association to Shape the Future of Agentic AI
Superagentic AI is proud to announce its membership in the UKAI Trade Association, the leading voice for the artificial intelligence industry in the United Kingdom. This milestone reflects Superagentic AI’s […]
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 […]
Superagentic AI is Coming to San Francisco: Exhibiting at ODSC AI West 2025
We’re beyond excited to share some big news: Superagentic AI is exhibiting at ODSC AI West 2025, the #1 AI Builders Conference, in SF taking place October 28–30, 2025 at […]
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 […]
Optimas + SuperOptiX: Global‑Reward Optimization for DSPy, CrewAI, AutoGen, and OpenAI Agents SDK
Optimization has been central to SuperOptiX from day one regardless it’s prompt, weights, parameters or compute. It began with DSPy-style programmatic prompt engineering and teleprompting as it was the only framework […]
SuperOptiX Memory: A Practical Guide for Building Agents That Remember
Modern AI agents are just limited to chatbots or prompt-in, answer-out. To feel coherent and genuinely helpful over time, they need to remember. Agent memory is the capability that lets […]