{
  "schema_version": "1.0",
  "id": "auto-3518047f01",
  "slug": "how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01",
  "url": "https://feed7.dev/p/how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01",
  "title": "How Evals and Prompts Shape Agent Behavior — Preetika Bhateja & Daniel Bump, YouTube Ads",
  "why_included": "Start with a few core tasks, inspect traces, change one component at a time, and protect a separate test set.",
  "summary": "Production agent behavior should be improved through a loop of tool design, small eval sets, trace review, and calibrated judges—not prompt tweaks based on one bad run.",
  "practical_implication": "Build evals early, inspect traces, and change one prompt, tool, rubric, or model component at a time. Compare human and LLM ratings, maintain a high-agreement golden set, and use ablations to establish whether a change actually improves the agent.",
  "agent_context": "The YouTube Ads team recommends optimizing a focused set of agent tools before larger evals, then starting with **a few core tasks** and testing both desired behavior and prohibited actions. Explanations help diagnose multi-dimensional outcomes such as accuracy and brand safety.\n\nBuild evals early, inspect traces, and change one prompt, tool, rubric, or model component at a time. Compare human and LLM ratings, maintain a high-agreement golden set, and use ablations to establish whether a change actually improves the agent.\n\nDo not tune against isolated nondeterministic failures. Keep a sparingly used **test set**, refresh it with production data, monitor judge disagreement, and define launch gates before iteration makes the rubric a moving target.",
  "source": {
    "name": "AI Engineer",
    "url": "https://www.youtube.com/watch?v=xyL2Ltkh-SA",
    "published_at": "2026-07-24T00:00:00.000Z"
  },
  "source_class": "video",
  "content_type": "Video",
  "layer": "benchmark",
  "domains": [
    "coding"
  ],
  "topics": [
    "agent-evals",
    "agent-reliability",
    "prompting"
  ],
  "verification": {
    "status": "source_linked",
    "label": "Source Linked",
    "method": "source_feed",
    "verified_at": null
  },
  "uncertainty": [],
  "lifecycle": "New",
  "published_at": "2026-07-24T00:00:00.000Z",
  "modified_at": "2026-07-24T00:00:00.000Z",
  "supersedes": [],
  "expires_at": null,
  "formats": {
    "html": "https://feed7.dev/p/how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01",
    "json": "https://feed7.dev/p/how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01.json",
    "markdown": "https://feed7.dev/p/how-evals-and-prompts-shape-agent-behavior-preetika-bhat-3518047f01.md"
  }
}