Memento-Skills: Build Self-Evolving AI Agents

“This course contains the use of artificial intelligence” Build the next generation of intelligent systems with Memento-Skills: Build Self-Evolving AI Agents , a cutting-edge, hands-on bootcamp designed for professionals who want to move beyond static AI and into self-improving, adaptive agent systems . This course teaches you how to design AI agents that learn from experience , continuously evolve their capabilities, and improve performance over time—without retraining underlying models. Traditional AI systems rely on fine-tuning models , but modern architectures are shifting toward memory-driven intelligence . In this bootcamp, you’ll master the paradigm of “Memory > Models” , where agents leverage structured memory, reusable skills, and feedback loops to evolve dynamically. You will learn how to design a Memento-Skills architecture , enabling agents to observe, reason, act, and improve autonomously. Throughout this 7-day intensive bootcamp , you will build a complete self-evolving AI agent system from scratch. Starting with a baseline stateless agent, you’ll progressively add capabilities such as structured logging , skill libraries , and intelligent routing systems . You’ll define what a “skill” is—combining prompts, workflows, and logic—and organize them into reusable, scalable components using JSON and Markdown-based architectures . A core focus of the course is building a robust skill retrieval and routing engine . You’ll go beyond simple embeddings and implement hybrid retrieval systems using FAISS or Chroma , keyword search (BM25) , and reranking techniques to ensure your agent selects the right capability for every task. This enables context-aware decision-making and dramatically improves reliability. You’ll then design multi-step workflows using proven agent patterns like Planner → Executor → Validator , enabling your system to handle complex, real-world tasks. With integrated tools and structured outputs, your agent will generate execution traces , manage state, and operate like a production-grade system. One of the most powerful aspects of this course is the implementation of a reflection and feedback system . Using LLM-as-a-judge , your agent will evaluate its own outputs, identify failures, and generate improvement suggestions. You’ll implement tip memory and skill memory , allowing your system to retain insights and refine behavior over time. Finally, you’ll build a skill evolution engine that enables your agent to rewrite existing skills or create new ones dynamically. With built-in guardrails , validation mechanisms , and rollback strategies , you’ll ensure your system improves safely without regression—bringing you closer to truly autonomous AI systems . By the end of this course, you will have built a production-ready, self-evolving AI agent , complete with memory systems , evaluation pipelines , and continuous learning loops . This is not just theory—you’ll walk away with a portfolio-grade project that demonstrates expertise in agentic AI , multi-agent systems , and intelligent automation . Whether you're an AI engineer, product leader, or innovator, this course equips you with the skills to build next-generation AI systems that don’t just respond—but learn, adapt, and evolve . Who this course is for: AI engineers and developers who want to build next-generation, self-evolving agent systems Product managers and technical leaders exploring agentic AI and intelligent automation Developers familiar with LLMs who want to go beyond prompts into memory + skills + learning loops Builders and innovators looking to create portfolio-grade AI projects with real-world impact Professionals interested in multi-agent systems, orchestration, and autonomous workflows Startup founders and indie hackers aiming to build adaptive AI-powered products Data scientists transitioning into applied agentic AI systems and architectures Anyone curious about how to build AI that learns, improves, and evolves over time (with guided, hands-on support)