--- title: Thinking in reasoners | daily.dev description: A framework for structuring AI agent systems around five design principles: give each reasoner a single cognitive job, set goals and verify outcomes rather... canonical: https://daily.dev/posts/thinking-in-reasoners-qwwobe2tz twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Thinking in reasoners | daily.dev og:description: A framework for structuring AI agent systems around five design principles: give each reasoner a single cognitive job, set goals and verify outcomes rather... og:url: https://daily.dev/posts/thinking-in-reasoners-qwwobe2tz og:image: https://api.daily.dev/og/posts/QWWoBE2tZ.png og:image:alt: Thinking in reasoners 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. # Thinking in reasoners **[AgentField.ai](https://daily.dev/sources/agentfield)** · 8 min read · 1 upvotes · 0 comments ## Summary A framework for structuring AI agent systems around five design principles: give each reasoner a single cognitive job, set goals and verify outcomes rather than scripting steps, choose between a lightweight classification call (app.ai) and a stateful tool-using agent (app.harness) deliberately, cap every loop and spawn with a hard numeric limit, and route prose to models but structured data to code. Each principle is illustrated with before/after code snippets using the AgentField framework, plus a decision table for choosing app.ai versus app.harness. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### When should I use a single-shot AI call versus a tool-using agent harness for a task? Use a single-shot call like app.ai for classifying, routing, or gating decisions where input fits in roughly 2k tokens and output is 2-4 flat fields with no tools needed; use a stateful harness like app.harness when the input is large and must be explored, output is narrative or a patch, or the task needs multi-turn reasoning with a shell, files, or sub-agents. A common fallback: start with the cheap classifier, add a confident boolean field, and escalate to the harness only when it returns false. _Developers choosing between lightweight classifiers and agentic harnesses can compare approaches like this one on daily.dev._ ### How do you prevent an autonomous AI agent loop from running up unbounded costs? Cap every loop and spawn with a hard integer limit rather than letting the model decide when it's done, since an adaptive system will always find a reason to go one level deeper. For sub-agent spawns, cap the count and dispatch children with budget=0 so they cannot recurse. For token spend, max_budget_usd is a hard ceiling only on the claude-code provider; on other providers, cap with max_turns and check result.cost_usd after the run since max_turns bounds turns, not dollars. _Teams building cost-controlled agent pipelines track patterns like hard loop caps on daily.dev._ ## Similar posts on daily.dev - [The architecture behind an AI tutor, four months in](https://daily.dev/posts/the-architecture-behind-an-ai-tutor-four-months-in-uqjo9alxn) · Medium · 4 upvotes · 0 comments - [Tools vs. Subagents: Building Effective AI Agents Without Over-Engineering](https://daily.dev/posts/tools-vs-subagents-building-effective-ai-agents-without-over-engineering-oonittab6) · Machine Learning Mastery · 1 upvotes · 0 comments - [How to build an AI agent that actually works](https://daily.dev/posts/how-to-build-an-ai-agent-that-actually-works-2rhdwrzec) · InfoWorld · 1 upvotes · 1 comments - [The Intelligible World of Agents](https://daily.dev/posts/the-intelligible-world-of-agents-bazldoigc) · Recorded Future Blog · 0 upvotes · 0 comments --- Tags: [#career](https://daily.dev/tags/career), [#ai-agents](https://daily.dev/tags/ai-agents), [#prompt-engineering](https://daily.dev/tags/prompt-engineering) [View this post on daily.dev](https://daily.dev/posts/thinking-in-reasoners-qwwobe2tz) ```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":"TechArticle","headline":"Thinking in reasoners","url":"https://daily.dev/posts/thinking-in-reasoners-qwwobe2tz","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/thinking-in-reasoners-qwwobe2tz"},"datePublished":"2026-10-02T17:45:22.164Z","dateModified":"2026-10-03T17:37:05.029Z","description":"A framework for structuring AI agent systems around five design principles: give each reasoner a single cognitive job, set goals and verify outcomes rather...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/7f53c00cb701575bc660f0cce88e9180?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/7f53c00cb701575bc660f0cce88e9180?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"AgentField.ai","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"AgentField.ai","logo":"https://media.daily.dev/image/upload/s--VLFHaedl--/f_auto/v1791063152/squads/33beeb91-1f14-4d9b-9cf8-db7d9f69160a?_a=BAMAMicg0","url":"https://daily.dev/squads/agentfield"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/thinking-in-reasoners-qwwobe2tz","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"career,ai-agents,prompt-engineering","timeRequired":"PT8M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"AgentField.ai","item":"https://daily.dev/squads/agentfield"},{"@type":"ListItem","position":3,"name":"Thinking in reasoners"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/thinking-in-reasoners-qwwobe2tz#faq","mainEntity":[{"@type":"Question","name":"When should I use a single-shot AI call versus a tool-using agent harness for a task?","acceptedAnswer":{"@type":"Answer","text":"Use a single-shot call like app.ai for classifying, routing, or gating decisions where input fits in roughly 2k tokens and output is 2-4 flat fields with no tools needed; use a stateful harness like app.harness when the input is large and must be explored, output is narrative or a patch, or the task needs multi-turn reasoning with a shell, files, or sub-agents. 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