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.
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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/programmingcentral/beyond-static-prompts-how-to-build-self-improving-ai-agents-with-closed-loop-skill-playbooks-583 |
| Publication time | Sat, 30 May 2026 20:00:00 +0000 |
| Retrieval time | 2026-05-30T20:29:45.642Z |
| Last seen | 2026-05-30T20:29:45.642Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | LTFFyS4FtUxn |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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...
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