Google Experts Share AI Agent Evaluation Best Practices

In the rapidly evolving world of AI agents, ensuring reliability and performance is paramount. Preetika Bhateja and Daniel Bump from Google's YouTube Ads team recently shared their insights on building effective evaluation systems for production agents at the AI Engineer World's Fair. Their talk, "Model Whisperers: How Evals and Prompts Shape Agent Behavior," highlighted key strategies and lessons learned from their experience with image and video models for YouTube ads.

The Challenge of Agent Reliability

Bhateja and Bump began by acknowledging the inherent difficulty in building AI agents. "Building an AI agent is hard. It's laborious. It takes a lot of time," stated Bhateja. She emphasized that making these agents reliable, ensuring they perform as intended in production and react predictably to various inputs, is an even greater challenge. This is where robust evaluation systems become essential.

Laying the Foundation: Tools and Critique Agents

The speakers stressed the importance of establishing a solid agent foundation. This starts with a focused set of LLM-friendly tools that are optimized for performance. "I would say it's important to first optimize these tools and make sure they're the best they can be before just jumping onto larger agent evals," advised Bump. He also suggested creating an independent critique agent with a remediation loop to build self-correction mechanisms and address limitations in the base toolset.

Once the core structure is in place, a strong evaluation system becomes critical. "A strong eval gives you a way of proving the value of changes you make as well as running ablation experiments on any changes you make," Bump explained. "I would say this is a very essential tool for climbing the quality ladder." Ultimately, agent reliability is a function of its capabilities, guardrails, and the effectiveness of its evaluations.

Defining "Good": The Role of Evals

Generative AI outputs are notoriously non-deterministic, meaning agents can behave inconsistently. This unpredictability necessitates a scalable way to measure and ensure desired outputs. Evals play a crucial role by defining what constitutes a "good" output. To scale effectively, these evals must be strict and measurable.

The "Vibing" Approach: Intuition Over Scale (Early On)

An interesting point raised was that, counterintuitively, "vibing", or engaging in non-scalable, intuition-based validation early on, can be beneficial. Instead of immediately building a comprehensive eval, the team found it more effective to first explore agent capabilities through a more manual, intuition-driven approach. This allows for quicker iteration, easier identification of failure patterns, and the ability to make radical changes to prompts or architecture without being hindered by a rigid evaluation framework.

This early-stage "vibe check" helps developers gain a deeper understanding of their agent's behavior, paving the way for more targeted improvements. The speakers cautioned against jumping to scaled, automated evaluations too early, as this can lead to significant ups and downs when trying to calibrate the eval itself while simultaneously changing the model.

Starting Small and Testing Negatives

The advice to "start early and start small" was a recurring theme. Developers don't need a massive golden set from day one. Instead, they should begin with a few core tasks that define the agent's primary goals. As the process evolves, a more detailed representation can be built. Crucially, testing for what the model doesn't do is as important as testing what it does do. Checking for negative outcomes ensures the agent is not only performing the task but also avoiding undesirable behaviors.

Scaling Up: Involving Teams and Leveraging LLM Judges

As the agent development progresses, involving more teams becomes necessary. With a larger dataset and more stakeholders, the focus shifts towards scalability, including the use of LLM raters or judges. To ensure the effectiveness of LLM judges, the team recommended several practices:

• Monitor Disagreements: Track where LLM judges disagree with human evaluations to identify potential biases or misunderstandings.
• Go Beyond Pass/Fail: Encourage spot checks for reasoning, intent, and multi-output scenarios (e.g., accuracy, brand safety). Explanations from raters are vital for agent improvement.
• High-Quality Expert Labels: Ensure a reliable "golden truth" set with high human-human agreement as a baseline for comparison.

Understanding Agent "Thinking" Through Traces

Bhateja shared an example where an agent failed to adhere to a prompt's rule about disclaimers never being removed. Even with a high pass rate in categorical evaluations, the agent's behavior was problematic. By examining the agent's "thinking" through its traces, the team discovered the agent was detecting the disclaimer but still proceeding to remove it. This highlights the importance of looking beyond simple pass/fail metrics and understanding the underlying reasoning of the agent.

Key Takeaways for Production-Ready Evals

The speakers summarized their key recommendations for building production-grade evaluation systems:

• Representativeness: Evals must accurately reflect what the product aims to achieve, adapting from MVP to production rollout stages.
• Evolution: Evaluation systems should be dynamic, incorporating learnings from new user data and evolving use cases.
• Curated Golden Sets: Develop high-quality, expert-audited golden sets that cover a broad range of scenarios and edge cases.
• Rater Training: Invest in training for human raters, providing clear templates, rubrics, and examples to ensure consistent and accurate judgments.
• Launch Metrics: Clearly define launch criteria and metrics early in the process to ensure readiness for deployment.

By focusing on these principles, teams can build more robust and reliable AI agents, navigating the complexities of generative AI development with greater confidence.