“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.