Your LLM Is Not an Agent. Your Framework Is Not Enough. You Need a Harness.
The article discusses the challenges faced when deploying AI agents built with large language models (LLMs). It emphasizes that while LLMs are capable, they require a structured operating environment, referred to as a 'Harness,' to function reliably in real-world applications. Without this framework, agents struggle with context retention, memory, and error handling, leading to inconsistent performance and user distrust.
- ▪AI agents often perform well in demos but fail in real-world applications due to lack of structure.
- ▪A 'Harness' is necessary to provide the operating environment that governs how AI agents operate.
- ▪Without a harness, agents can experience context amnesia, infinite loops, and tool failures.
3 outlets in our directory ran this story, first to last over 24 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ Webwright: A terminal is all you need for web agents — Github
- ▪ Webwright: A Terminal Is All You Need for Web Agents — Microsoft Research
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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/sreeni5018/your-llm-is-not-an-agent-your-framework-is-not-enough-you-need-a-harness-321j |
| Publication time | Mon, 25 May 2026 06:20:59 +0000 |
| Retrieval time | 2026-05-25T06:37:36.378Z |
| Last seen | 2026-05-25T06:37:36.378Z |
| 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 | J1-QXfgLYElF · 3 stories |
| 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 === 1829954) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Seenivasa Ramadurai Posted on May 25 Your LLM Is Not an Agent. Your Framework Is Not Enough. You Need a Harness. Introduction Every team building with AI agents hits the same wall. The demo works beautifully. The agent answers questions, calls tools, produces results. Then you ship it and the cracks appear it loses track of what it was doing, burns through API calls in circles, ignores boundaries it should respect, forgets context from five minutes ago. Users lose trust.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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