Name: Memento-Skills: Build Self-Evolving AI Agents
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What You'll Learn

Design and build self-evolving AI agents that learn from experience without retraining models
Implement the Memento-Skills architecture (memory-driven, skill-based intelligence systems)
Create and manage a reusable AI skill library (prompt + workflow + logic design)
Build a hybrid skill retrieval and routing engine using vector search, keyword search, and reranking
Develop multi-step agent workflows using Planner → Executor → Validator patterns
Implement LLM-as-a-judge evaluation systems to assess and improve agent outputs
Design reflection and feedback loops for continuous agent learning and improvement
Build a skill evolution engine that rewrites or creates new skills dynamically
Apply guardrails, validation, and rollback strategies to prevent regression in evolving systems
Deploy a production-grade agent system with logging, memory, and performance tracking

Requirements

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)

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An experienced instructor with expertise in this field and a passion for teaching.

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