--- title: Continual Learning for Agents | daily.dev description: Production AI agents typically run on closed frontier models where developers cannot modify weights, shifting continual learning to the harness and context... canonical: https://daily.dev/posts/continual-learning-for-agents-rw41ufszn twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Continual Learning for Agents | daily.dev og:description: Production AI agents typically run on closed frontier models where developers cannot modify weights, shifting continual learning to the harness and context... og:url: https://daily.dev/posts/continual-learning-for-agents-rw41ufszn og:image: https://api.daily.dev/og/posts/Rw41UFSzN.png og:image:alt: Continual Learning for Agents og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Continual Learning for Agents **[Andrew M](https://daily.dev/sources/dup6ye036hupdshygexyu)** · [@andrewma](https://daily.dev/andrewma) · 1 min read · 1 upvotes · 0 comments ## Summary Production AI agents typically run on closed frontier models where developers cannot modify weights, shifting continual learning to the harness and context levels. Replit's team addressed this by building ViBench, a benchmark that evaluates functional app-building success from natural-language specs, and Telescope, an automated system that clusters production failure traces into actionable issue groups for iterative improvement. ## Content Most production agents run on closed frontier models, so developers cannot update model weights and must focus instead on harness-level and context-level continual learning. To achieve this at Replit, the team built ViBench to evaluate functional app-building success from natural-language specs alongside Telescope, an automated system that clusters production failure traces into actionable issue groups. ## Similar posts on daily.dev - [Coding agents are only as good as the signals you feed them](https://daily.dev/posts/coding-agents-are-only-as-good-as-the-signals-you-feed-them-9euzsy5sw) · The New Stack · 1 upvotes · 0 comments - [Building and Deploying AI Agents in Production: Lessons I Learned](https://daily.dev/posts/building-and-deploying-ai-agents-in-production-lessons-i-learned-fmxhggdbm) · Medium · 1 upvotes · 0 comments - [Effective harnesses for long-running agents](https://daily.dev/posts/effective-harnesses-for-long-running-agents-7clzunmmu) · Hacker News · 2 upvotes · 0 comments - [Evaluating AI Agents in Practice: Benchmarks, Frameworks, and Lessons Learned](https://daily.dev/posts/evaluating-ai-agents-in-practice-benchmarks-frameworks-and-lessons-learned-tohpewvfd) · InfoQ · 2 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents) [View this post on daily.dev](https://daily.dev/posts/continual-learning-for-agents-rw41ufszn) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"DiscussionForumPosting","mainEntityOfPage":"https://daily.dev/posts/continual-learning-for-agents-rw41ufszn","headline":"Continual Learning for Agents","text":"Production AI agents typically run on closed frontier models where developers cannot modify weights, shifting continual learning to the harness and context levels. 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