Defining Success: Key Metrics for Evaluating Your LLM and AI Agents

The age of AI is here. Developers are building incredible applications powered by Large Language Models (LLMs) and intelligent agents. But as we move from exciting demos to production systems, a critical question emerges: How do you know if your AI is actually any good?

"It feels right" isn't a strategy. To build reliable, trustworthy, and high-performing AI, you need to move beyond gut feelings and embrace systematic evaluation. Shipping with confidence requires defining what success looks like and measuring it rigorously.

This post will explore the key metrics you need to track to ensure the quality, accuracy, and reliability of your AI functions, workflows, and agents.

Beyond Unit Tests: Why AI Evaluation is Different

In traditional software development, we rely on unit tests. They are binary and deterministic: a function either produces the expected output or it fails. But AI, especially LLMs, is non-deterministic. The same prompt can yield slightly different results every time.

A unit test can check if 2 + 2 = 4. An AI evaluation needs to measure if a response is helpful, if its tone is appropriate, or if its summary is accurate. These are not simple pass/fail scenarios; they exist on a qualitative spectrum. This distinction is why specialized AI evaluation platforms are essential for a robust MLOps lifecycle.

Core Metrics for AI and LLM Evaluation

To effectively measure your AI's performance, you need a balanced scorecard of metrics. Here are the most critical ones to consider.

1. Accuracy and Factual Correctness

This is the bedrock of many AI applications. Is the model providing information that is true and verifiable?

• Why it matters: For Q&A bots, research assistants, or any system presenting facts, inaccuracies erode user trust and can have serious real-world consequences. In Retrieval-Augmented Generation (RAG) systems, this means ensuring the answer is grounded in the provided source documents.
• How to measure: Compare the AI's output against a "golden dataset" of correct answers. Use automated checks, other LLMs as judges, or human review to score for factual precision.

2. Relevance and Helpfulness

A factually correct answer that doesn't address the user's intent is useless. Helpfulness measures how well the AI's response satisfies the user's underlying need.

• Why it matters: Users interact with AI to solve a problem or get a task done. A response that is on-topic but unhelpful leads to frustration and abandonment.
• How to measure: This metric often requires nuanced judgment. It's measured by evaluating if the response directly answers the user's explicit or implicit question, provides actionable steps, or moves the conversation forward productively.

3. Tone, Style, and Brand Voice

Does your AI agent sound like it's part of your brand? Whether it needs to be professional, empathetic, witty, or formal, consistency in tone is key for a good user experience.

• Why it matters: For any customer-facing application, the AI agent is an ambassador for your brand. A mismatched tone can feel jarring and unprofessional, undermining the user's perception of your product.
• How to measure: Define style guidelines and score the AI's output against them. This can be done using LLM-based evaluators trained to recognize specific stylistic attributes.

4. Safety, Bias, and Toxicity

A non-negotiable for any production system is ensuring the AI does not produce harmful, biased, inappropriate, or toxic content.

• Why it matters: Failing to control for safety opens up significant brand risk, creates negative user experiences, and can perpetuate harmful societal biases.
• How to measure: Test the agent against a comprehensive dataset of adversarial prompts designed to elicit undesirable behavior. Use content moderation APIs and specialized classifiers to flag any safety violations.

5. Latency and Cost

Quality must be balanced with operational reality. How long does the user have to wait for a response, and how much does each generation cost?

• Why it matters: High latency leads to a poor user experience, while high costs can make an application economically unviable at scale.
• How to measure: Track the end-to-end response time for every generation. Monitor token consumption and API costs to understand the financial footprint of your AI system.

From Metrics to a System: The Evals.do Approach

Defining metrics is the first step. The next, more critical step is implementing a system to measure, monitor, and enforce them continuously. This is where an agentic workflow platform like Evals.do becomes indispensable.

Evals.do allows you to treat your evaluations as code, integrating them directly into your development lifecycle. Instead of running ad-hoc, manual checks, you can automate performance testing for everything from a single function to a complex, multi-step agent.

Imagine you're developing a customer support agent. You can define an evaluation run that tests it against 150 different customer scenarios. With Evals.do, you can get a clear, actionable report like this:

{
  "evaluationRunId": "run_a3b8c1d9e0f7",
  "evaluationName": "Customer Support Agent Evaluation",
  "status": "Completed",
  "overallResult": "FAIL",
  "summary": {
    "totalTests": 150,
    "passed": 135,
    "failed": 15,
    "passRate": 0.9
  },
  "metricResults": [
    {
      "name": "accuracy",
      "averageScore": 4.1,
      "threshold": 4.0,
      "result": "PASS"
    },
    {
      "name": "helpfulness",
      "averageScore": 4.3,
      "threshold": 4.2,
      "result": "PASS"
    },
    {
      "name": "tone",
      "averageScore": 4.4,
      "threshold": 4.5,
      "result": "FAIL"
    }
  ]
}

This report immediately tells you that while the agent is accurate and helpful, it failed the evaluation because its tone didn't meet the required threshold. By integrating this into your CI/CD pipeline, you can automatically prevent this underperforming version from being deployed, protecting your users and your brand.

Ship with Confidence

In the world of AI development, what you can't measure, you can't improve. Building a great AI product requires a disciplined commitment to quality assurance. By defining clear metrics and implementing a robust evaluation framework, you can move from guesswork to certainty.

Ready to evaluate your AI's performance from end-to-end? Learn how Evals.do can help you test, measure, and ensure the quality of your AI systems.