--- title: Model Whisperers How Evals and Prompts Shape Agent... description: Google engineers from the YouTube Ads team share practical lessons on building evaluation systems for AI agents. Key insights include starting with... canonical: https://daily.dev/posts/model-whisperers-how-evals-and-prompts-shape-agent-behavior-preetika-bhateja-daniel-bump-google-hxmplvitw twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Model Whisperers How Evals and Prompts Shape Agent Behavior — Preetika Bhateja & Daniel Bump, Google | daily.dev og:description: Google engineers from the YouTube Ads team share practical lessons on building evaluation systems for AI agents. Key insights include starting with... og:url: https://daily.dev/posts/model-whisperers-how-evals-and-prompts-shape-agent-behavior-preetika-bhateja-daniel-bump-google-hxmplvitw og:image: https://api.daily.dev/og/posts/HXMpLVItW.png og:image:alt: Model Whisperers How Evals and Prompts Shape Agent Behavior — Preetika Bhateja & Daniel Bump, Google 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. # Model Whisperers How Evals and Prompts Shape Agent Behavior — Preetika Bhateja & Daniel Bump, Google **[AI Engineer](https://daily.dev/sources/aidotengineer)** · 19 min read · 0 upvotes · 0 comments ## Summary Google engineers from the YouTube Ads team share practical lessons on building evaluation systems for AI agents. Key insights include starting with intuition-based 'vibing' before scaling to formal evals, building small golden sets and expanding gradually, using agent traces to debug unexpected behavior, getting explanations from human raters beyond pass/fail, calibrating LLM-as-judge against human raters, focusing on failure patterns rather than isolated runs, and defining clear launch criteria including acceptable regression thresholds. The talk emphasizes that a reliable agent depends on strong tooling foundations, guardrails, and a continuously evolving eval system. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [The Anatomy of Harness Engineering: How to Evaluate, Iterate, and Guard AI Coding Agents](https://daily.dev/posts/the-anatomy-of-harness-engineering-how-to-evaluate-iterate-and-guard-ai-coding-agents-m4zyf0sa6) · Google Developers · 1 upvotes · 1 comments - [AI Evals for Solo Developers 2026: A Practical Guide](https://daily.dev/posts/ai-evals-for-solo-developers-2026-a-practical-guide-ms6ijzdsf) · Alex CloudStar · 0 upvotes · 0 comments - [Building an internal agent: Evals to validate workflows](https://daily.dev/posts/building-an-internal-agent-evals-to-validate-workflows-iz9kwr8do) · Irrational Exuberance · 1 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 - [From "Vibe Checks" to Continuous Evaluation: Engineering Reliable AI Agents](https://daily.dev/posts/from-vibe-checks-to-continuous-evaluation-engineering-reliable-ai-agents-5ewhhupym) · Google Cloud · 2 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#genai](https://daily.dev/tags/genai), [#prompt-engineering](https://daily.dev/tags/prompt-engineering) [View this post on daily.dev](https://daily.dev/posts/model-whisperers-how-evals-and-prompts-shape-agent-behavior-preetika-bhateja-daniel-bump-google-hxmplvitw) ```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":"Model Whisperers How Evals and Prompts Shape Agent Behavior — Preetika Bhateja & Daniel Bump, Google","url":"https://daily.dev/posts/model-whisperers-how-evals-and-prompts-shape-agent-behavior-preetika-bhateja-daniel-bump-google-hxmplvitw","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/model-whisperers-how-evals-and-prompts-shape-agent-behavior-preetika-bhateja-daniel-bump-google-hxmplvitw"},"datePublished":"2026-07-24T21:00:37.793Z","dateModified":"2026-07-24T21:01:37.027Z","description":"Google engineers from the YouTube Ads team share practical lessons on building evaluation systems for AI agents. 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