EDD: The Science of Improving AI Agents // Shahul Elavakkattil Shereef // Agents in Production 2025

Building and consistently improving AI agents requires a structured, scientific approach rather than relying on intuition or “vibe-based” iteration. Eval-Driven Development (EDD) provides this framework, applying the scientific method—hypothesize, test, validate, and act—to the development lifecycle. This is crucial for collaborative projects where individual intuitions can differ, ensuring that teams align on definite goals and metrics for what constitutes good and bad agent behavior.

Agent evaluation can be broken down into two essential components: quantitative and qualitative analysis.

Quantitative Evaluation: Measuring What Matters

Quantitative evaluation focuses on measurable outcomes. For AI agents, this primarily involves four factors:

1. Metrics: To quantify agent performance.
2. Consistency: To ensure reliable behavior across multiple runs.
3. Cost: The computational expense of running the agent.
4. Latency: The time taken to produce a result.

When selecting metrics, the primary goal is to find an accurate proxy for user satisfaction.

Choosing the Right Metrics

• Start with End-to-End Evaluation: Focus on metrics that measure the performance of the system as a whole. This is what the user ultimately cares about, whether it’s a text-to-SQL agent, a software development agent, or a deep research agent. An end-to-end metric is the closest proxy to user satisfaction.

• Prioritize Strong Proxies: It’s more effective to use one or two strong proxies for success rather than multiple weak proxies. Weak proxies, such as helpfulness, tone, or coherence, are meta-information that don’t directly correlate with the user’s goal. A strong proxy, like goal success rate or execution accuracy, provides a clear, direct signal of the agent’s performance.

• Examples of Strong Proxies:

  • For a Text-to-SQL Agent: Execution accuracy is a strong proxy. The generated SQL must execute and produce the same output as the ground-truth query. Syntactic or semantic correctness are weaker proxies, as a syntactically correct query can still return invalid results.
  • For a Deep Research Agent: An LLM-as-judge, guided by detailed rubrics and grading notes, can score the final report’s quality.
  • For a Conversational Agent: Goal success rate is a strong proxy, measuring whether the user’s underlying goal was achieved during the interaction.

Aligning LLMs as Judges

When using LLMs for evaluation, they must be aligned with human judgment. An unaligned LLM can be unreliable. The key is to “teach” the LLM the decision boundary that a human reviewer uses. This involves an iterative process:

1. Review and Correct: Manually review the LLM’s initial evaluations.
2. Prompt Engineering: Refine the evaluation prompt based on incorrect judgments.
3. Few-Shot Examples: Provide the LLM with examples of failures and the correct evaluations to guide its reasoning. This creates a feedback loop that improves the correlation between the LLM’s scores and human assessment.

Qualitative Evaluation: Understanding the “Why”

Quantitative metrics tell you what percentage of your system fails, but not why it fails or how to fix it. If an agent has an 80% success rate, qualitative evaluation focuses on turning the 20% of failures into actionable insights. This is achieved through a two-step process.

1. Error Analysis

In this step, you inspect the failed outputs to identify and categorize what went wrong. This involves manually reviewing the failures and hypothesizing the root causes, such as:

• Incorrect reasoning
• Tool misuse
• Hallucination
• Insufficient depth in an answer

2. Attribution Analysis

Agentic systems are autonomous and multi-step, which makes debugging complex. Even if you know what the error is, pinpointing which part of the agent’s process caused it is challenging. Attribution analysis connects the identified error to a specific step or a series of steps in the agent’s reasoning chain.

For example, if a research agent produces a weak final report (the error), attribution analysis would trace back through the agent’s steps to determine if the cause was a faulty memory mechanism, a poor retrieval step, or incorrect tool usage. By attributing the error to a specific component or step, you can form a precise hypothesis for improvement.

This process can be challenging for complex agents, like browser agents, which can have hundreds of steps. Manually tracing an error back through a long execution log can be time-consuming but is essential for targeted improvements.

By combining rigorous quantitative evaluation with deep qualitative analysis, teams can move beyond guesswork and systematically iterate to build more robust and reliable AI agents.

• Tags:
• AI Agents
• Feature
• LLM Evaluation
• MLOps
• Eval-Driven Development
• Ragas
• Error Analysis
• Attribution Analysis