How Evals and Prompts Shape Agent Behavior — Preetika Bhateja & Daniel Bump, YouTube Ads
Building reliable AI agents requires a comprehensive evaluation loop that extends far beyond prompt engineering. The process involves optimizing foundational tools, establishing rigorous evaluation workflows (Evals), and iteratively refining behavior using feedback signals rather than simple scorecards. Key strategies include starting with small-scale, intuition-based evaluations to identify failure patterns early, analyzing agent trace logs for reasoning failures, and focusing on systemic patterns of error rather than isolated instances.
Key takeaways
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Agent Reliability Loop
Agent behavior is a function of its capabilities, guardrails, and the evaluation process. The system relies on an iterative loop: Prompts $\rightarrow$ Evals $\rightarrow$ Iteration $\rightarrow$ Feedback.
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Foundational Optimization
Before large-scale agent evals, optimize the core set of LLM-friendly tools. Consider implementing an independent critique agent with a remediation loop to fill gaps in base tool limitations.
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Starting Small (Vibing)
3:35
Instead of immediately building comprehensive, scalable evals, start with an intuition-based approach ('vibing'). This allows for quick iteration and radical architectural changes without being hindered by strict scalability requirements.
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Deep Failure Analysis
7:50
To understand failures, analyze agent trace logs to see the reasoning behind a conclusion (e.g., why an agent removed a disclaimer despite explicit instructions). This is more valuable than simple pass/fail metrics.
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Scaling Evals and Consensus
10:10
When involving human raters (scale raiders), provide clear rubrics, detailed examples, and train the team on expected outcomes. For complex systems, collect explanations from raters rather than just a binary pass/fail rating.
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Focusing on Patterns
15:30
When diagnosing model performance degradation (regressions), focus on identifying systemic patterns of failure across the entire golden set, rather than hyper-fixingating on single, isolated examples.