Common evaluation approaches

When you build AI agents, you need reliable ways to test and measure their performance. Evaluation strategies help you generate test data, grade agent responses, and make informed decisions about your agent's quality.

This article describes common evaluation approaches and when to use each one. To optimize cost, performance, and quality, combine multiple approaches and platforms rather than rely on a single evaluation method.

Generating request-response pairs for grading

This section describes three common approaches for generating request-response pairs to simulate real-world interactions: echo, historical replay, and synthesized personas. Each approach has its own advantages and limitations, making them suitable for testing across various scenarios.

Echo

An agent chat replays a static list of multi-turn prompts that map to a scenario, word for word.

Pros: Low cost. Provides fair comparisons when you change only one aspect of an agent, such as incremental model upgrades or single tool changes.

Cons: Because the evaluation uses a static list of prompts, it can't adjust to different responses that agents provide during the conversation. Later prompts might not be relevant to the current conversation context.

Ideal for: Single-turn scenarios and deterministic checks. Use this method to check if citations display correctly, if the tool call triggers correctly, and for simple conversations where context doesn't cause divergence.

Example scenarios that work well:

Turn 1: Upload a document (binary pass or fail check)
Turn 1: Generate an image for this content (similarity check)
Turn 2: Now generate a caption. (similarity check)

Historical replay

Evaluate each turn in the context of prior prompts and responses for each request.

Pros: Partially solves the divergence problem in multi-turn conversations by identifying where and how much each turn diverges from the ideal path.

Cons: Still can't handle dynamic multi-turn conversations like learning, or account for dynamic RAG (Retrieval Augmented Generation) changes (for example, web search).

Ideal for: Comparison treatments or model changes to understand divergence from original behavior at each turn.

Synthesized personas (scenario based)

A human or agentic actor generates a conversation in real time based on a scenario and a persona.

Pros: You can dynamically assess complex scenarios (for example, act as a tutor).

Cons: Grading accuracy of answers requires nuance, and you need to consider the cost of a language model or human tester.

Grading the responses

After you capture request-response pairs, grade the overall quality and performance of the agentic system. Common grading approaches include code-based graders, language models as judges, and human graders.

Code-based graders

Examples: Regex, binary pass-fail, unit testing, calculated vector similarity, telemetry based (performance, capacity, cost).

Pros: Mature solutions and frameworks exist. For example, regex, lint, and UX test pipelines. You can easily verify deterministic checks.

Cons: It's difficult to accurately evaluate nuance or qualitative aspects of an agent, like tone and accuracy.

Language model as judge

Pros: Enables scenario-based testing at scale. Flexible enough to encode a wide array of user preferences.

Cons: Overreliance on only language model base evaluation or a limited set of models and grounding data can bring entropy into the evaluation process.

Human graders

Pros: Provides the best qualitative evaluation.

Cons: Slow and expensive. Requires human experts to dedicate time away from their day jobs.

Translating evaluation results into decisions

Agents disrupt existing feasibility and return on investment (ROI) frameworks as solution thinking evolves towards multi-agent, Agent 365, or digital worker concepts. Consider the following factors:

The nondeterministic nature of language models requires a shift from static pass or fail success criteria and unit test-based measurements to percentage-based evaluations.
The ROI for an agent includes impact beyond a standalone solution or single process flow as modular tools (MCP) or Agent2Agent (A2A) multi-agent ecosystems scale beyond a single use case.

The following sections describe best practices for translating evaluation results into informed decisions about your agent's architecture and deployment strategy.

Establish evaluation metrics

Establish a baseline measurement of the success of the existing system, even if it's manual. For example, ticket routing with existing support providers doesn't have a 100% success rate even when humans or process automation are used.

Evaluation metrics should be specific to the business outcome. For example, when assessing a ticket routing solution, evaluate both time to resolution (TTR) and routing accuracy to prioritize tradeoffs between architectures. One solution might offer higher accuracy with a longer TTR, which might be less desirable than a faster but slightly less accurate agentic solution.

Before you build any solution, complete a proof of concept evaluation of the language model, API, or agent type. This evaluation helps you understand if the proposed solution increases the baseline success rate by a statistically significant percentage, or if it provides an equivalent success rate reliably with time or cost savings.

Move away from serial development flows

Legacy approaches to building agents use a sequential or serial thought model. This model frequently leads to dead ends. The concept of "upgrading" agents from declarative agents to custom agents to "pro-code" agents supports this thought model. It's sequential and serial in nature.

This approach creates the perception of a dead end or regression when "upgrading" an agent, even though the underlying orchestrators and language models are different. Evaluation of success criteria in this manner doesn't account for the multivariate nature of agent solutions.

When you interpret evaluation results, resist the urge to go for an averaged or low-friction score like a radar plot. Select agents based on their ability to bias in favor of the one or two specific qualities needed for success.

In the following example, even though the radar plot suggests that Solution A is the better choice because it covers more surface area, for an HR solution Solution B produces more compliant results. Solution B is the better choice when request volume and business priority (sales motions) aren't major factors for success.

Use high friction visualizations, like column charts or decision frameworks, to further highlight dimensions most critical to success for a particular use case. These tools clarify when to prioritize search relevance over recall, or time-to-response, over context size, performance over cost, and similar considerations.

Note

Hybrid evaluation approaches where human graders audit and further refine the reasoning of the language model judges can provide benefits of both approaches while reducing individual constraints.

Test plan creation

Evaluation criteria and results vary by platform and solution. For guidance on test plan creation, consult the following resources:

Feedback

Was this page helpful?

Last updated on 2026-02-21