Beyond Static Prompts: How to Build Self-Improving AI Agents with Closed-Loop Skill Playbooks
The article discusses the evolution of AI agents from static prompts to self-improving systems. It emphasizes the need for AI skills to be dynamic and capable of adapting through closed-loop feedback mechanisms. By using the Hermes Agent framework, developers can create agents that learn from their experiences and improve their performance over time.
- ▪The current wave of AI development is moving towards fully autonomous systems.
- ▪Traditional AI agents often fail due to static skill definitions that do not adapt to real-world changes.
- ▪Self-improving agents can utilize closed-loop feedback systems to enhance their capabilities and performance.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3681483) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Programming Central Posted on May 30 Beyond Static Prompts: How to Build Self-Improving AI Agents with Closed-Loop Skill Playbooks #hermesagent #ai #python Book 18 Python & AI Masterclass (10 Part Series) 1 Beyond the Prompt: How to Build Stateful AI Agents with Persistent Memory and Self-Learning Loops 2 Beyond the Prompt: How to Build an AI Agent That Actually Learns From Its Mistakes ... 6 more parts...
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
