Evaluation datasets form the foundation for assessing AI agent quality, yet many organizations struggle to build datasets that accurately reflect real-world performance. Research shows that effective agent evaluation requires curated datasets with balanced examples representing normal operations, complex scenarios, and edge cases. Without robust datasets, teams cannot reliably measure agent improvements, catch regressions, or validate production readiness.
This guide provides practical tips and proven techniques for building evaluation datasets that drive meaningful insights into agent behavior. Whether you are developing single-turn assistants or complex multi-agent systems, these strategies will help you create datasets that enable systematic quality improvement.
Start with Comprehensive Trace Logging
Building effective evaluation datasets begins before you write your first test case. Industry best practices emphasize that you cannot evaluate what you cannot observe, making comprehensive logging the essential first step.
Trace logging captures complete records of agent interactions—every event from user input to tool calls, reasoning steps, and system responses tied to one session or workflow. A complete trace should include the original user input and configuration, intermediate reasoning steps showing how the agent approached the task, tool calls with their inputs and outputs, and the final result delivered to the user.
Start logging traces from day one to enable future analysis and debugging. This moves evaluation beyond simple outcome assessment to understanding the complete decision path agents take. With comprehensive traces, you can debug issues systematically, monitor performance across different scenarios, and retroactively investigate new error types as they emerge.
Maxim's Observability suite enables teams to track and debug live quality issues with distributed tracing, creating multiple repositories for production data that can be logged and analyzed comprehensively. This trace data becomes the raw material for building high-quality evaluation datasets.
Curate Balanced Datasets Representing Real-World Diversity
Effective evaluation datasets must reflect the full spectrum of scenarios agents will encounter in production. Research demonstrates that robust evaluation datasets should include at least 30 evaluation cases per agent, covering success cases, edge cases, and failure scenarios.
The challenge lies in achieving balance across multiple dimensions simultaneously. Your dataset should represent different user intents, varying input complexity, diverse conversation patterns, and critical edge cases that could cause failures.
Start by categorizing test cases into three tiers. Normal operations represent common, straightforward interactions that agents should handle reliably. These establish baseline performance expectations and ensure agents meet fundamental requirements without excessive complexity.
Complex scenarios test agent capabilities under challenging but realistic conditions. These might involve ambiguous user requests, multi-step reasoning requirements, integration of information from multiple sources, or handling of conflicting constraints. Complex cases reveal whether agents can handle the nuanced situations that differentiate good from great performance.
Edge cases and failure scenarios intentionally push agents to their limits. Test behavior with malformed inputs, adversarial queries designed to confuse reasoning, resource constraints or timeout conditions, and scenarios where no correct answer exists. Understanding failure modes helps establish appropriate guardrails and escalation paths.
Maxim's Data Engine enables teams to import datasets including images, continuously curate and evolve them from production data, and create data splits for targeted evaluations. This systematic approach to dataset curation ensures comprehensive coverage across all relevant dimensions.
Build Ground Truth Labels Through Expert Review
Raw interaction traces provide observational data, but evaluation requires ground truth labels that define correct behavior. Best practices recommend gathering a balanced set of examples and establishing ground truth through subject matter expert review.
Ground truth labeling involves several considerations. First, define clear criteria for what constitutes correct behavior. For some applications, correctness is objective—the agent either completed the task or failed. For others, quality exists on a spectrum requiring nuanced judgment about helpfulness, clarity, or appropriateness.
Second, establish consistent labeling guidelines that multiple reviewers can apply reliably. Document edge cases and ambiguous scenarios with specific guidance on how to label them. This reduces labeling inconsistency that would otherwise undermine dataset quality.
Third, validate inter-rater reliability by having multiple experts label the same examples independently. High agreement indicates clear criteria and consistent application. Low agreement signals that criteria need refinement or that additional training is required for reviewers.
Maxim's unified evaluation framework enables teams to conduct human evaluations for last-mile quality checks and nuanced assessments, ensuring expert judgment is systematically incorporated into evaluation workflows.
Leverage Production Data for Realistic Test Coverage
Synthetic test cases serve important purposes, but production data provides irreplaceable insights into real user behavior patterns. Databricks research emphasizes the importance of forwarding traces from development, pre-production, and production environments for expert labeling to build evaluation datasets grounded in actual usage.
Production data reveals scenarios that teams rarely anticipate during design. Users phrase requests in unexpected ways, combine features in novel patterns, and encounter edge cases that synthetic testing misses. Mining production logs for evaluation cases ensures tests reflect reality rather than assumptions.
However, production data requires careful curation. Not all production interactions make good test cases. Prioritize examples that represent common user journeys, expose previous failures or edge cases, demonstrate successful complex interactions, and reflect diverse user populations and use cases.
Implement systematic processes for converting production traces into evaluation cases. Tag interesting interactions for review, establish workflows for expert labeling, maintain version control as datasets evolve, and document rationale for including specific examples.
Maxim's Observability platform enables teams to monitor real-time production logs and curate datasets for evaluation needs, bridging the gap between production operations and systematic quality assessment.
Distinguish Between Single-Turn and Multi-Turn Evaluation Needs
Agent architectures vary significantly in their interaction patterns, and evaluation datasets must reflect these differences. Industry experts emphasize that single-turn and multi-turn agents require fundamentally different evaluation strategies and test case designs.
Single-turn agents complete tasks within one interaction cycle. They receive input, process it, potentially call tools, and return results without maintaining conversation state. Evaluation datasets for single-turn agents focus on input diversity, tool selection accuracy, parameter correctness, and output quality for isolated requests.
Multi-turn agents engage in extended conversations, maintaining context across multiple exchanges. They build understanding progressively, clarify ambiguities through follow-up questions, and adapt strategies based on user feedback. Evaluation datasets for multi-turn agents must test conversation flow, context retention across turns, appropriate use of clarification, and graceful handling of topic shifts.
When building multi-turn evaluation datasets, design conversation trajectories that test specific capabilities. Create sequences that require context from earlier turns, test recovery when the agent misunderstands initial input, validate appropriate escalation when tasks exceed capabilities, and ensure consistent personality and tone across extended interactions.
Maxim's Simulation platform enables teams to simulate customer interactions across real-world scenarios and evaluate agents at a conversational level, analyzing the trajectories agents choose and assessing whether tasks complete successfully across multi-turn dialogues.
Implement Continuous Dataset Evolution
Evaluation datasets are not static artifacts created once and forgotten. Best practices emphasize that evaluation continues after release, as real user interactions surface new behavior patterns that differ from test data assumptions.
Establish processes for continuously evolving datasets as your understanding of agent behavior deepens. When production monitoring identifies failure patterns, add representative cases to evaluation datasets. When users discover edge cases, incorporate them into systematic testing. When requirements change, update datasets to reflect new expectations.
Version control becomes critical for dataset evolution. Maintain clear versioning that links dataset versions to agent versions being tested. Document what changed between versions and why. Track which test cases revealed specific issues. This historical context helps teams understand how evaluation practices evolved alongside agent capabilities.
Balance dataset growth with quality maintenance. As datasets expand, periodically review older test cases to ensure they remain relevant. Remove duplicates or obsolete cases. Consolidate similar scenarios. The goal is a lean, high-signal dataset rather than an unwieldy collection of every possible test case.
Maxim's Data Engine facilitates continuous dataset evolution by enabling teams to curate data from production logs, enrich it using human-in-the-loop workflows, and maintain organized datasets that grow strategically rather than haphazardly.
Establish Clear Success Criteria and Metrics
Robust evaluation datasets require more than just test cases—they need clear definitions of success that enable objective assessment. Define specific, measurable criteria that evaluators can apply consistently across all test cases.
For task completion metrics, establish binary or graduated scales that indicate whether agents successfully completed assigned tasks. For response quality, define dimensions like accuracy, helpfulness, clarity, and appropriateness with concrete scoring rubrics. For efficiency metrics, measure resource usage, number of tool calls, or conversation length required to complete tasks.
Document edge case handling explicitly. When agents encounter impossible requests or ambiguous inputs, what constitutes appropriate behavior? Clear criteria prevent evaluators from applying inconsistent judgments based on personal preferences.
Research shows that effective evaluation combines 3-5 metrics mixing component-level measurements with at least one end-to-end metric focused on task completion. This balanced approach prevents over-indexing on narrow measures while maintaining comprehensive quality assessment.
Maxim's evaluation framework provides off-the-shelf evaluators and custom evaluation creation, enabling teams to measure quality quantitatively using AI, programmatic, or statistical evaluators suited to specific assessment needs.
Integrate Human-in-the-Loop Workflows
Automated evaluation scales efficiently, but human judgment remains essential for capturing nuanced quality dimensions that automated systems miss. Best practices recommend integrating human review through structured workflows rather than ad-hoc processes.
Design human review to focus where it provides maximum value. Use automated evaluation for objective criteria that computers assess reliably. Reserve human review for subjective dimensions like appropriateness, tone, creativity, or cultural sensitivity. This division of labor optimizes both efficiency and quality.
Structure human review with clear interfaces and guidance. Provide reviewers with complete context including user intent, agent responses, and relevant conversation history. Present specific questions or rating scales rather than open-ended assessment. Capture rationale for judgments to inform future improvements.
Collect diverse reviewer perspectives when appropriate. For applications serving varied user populations, ensure reviewers represent relevant demographic diversity. For domain-specific applications, involve subject matter experts with appropriate credentials.
Maxim's evaluation platform enables systematic human evaluation for last-mile quality checks, ensuring expert judgment integrates seamlessly with automated assessment workflows.
Test Against Benchmark Datasets and Standards
While custom datasets tailored to specific applications remain essential, testing against established benchmarks provides valuable comparative context. Industry research demonstrates that standardized benchmarks enable comparison of agent capabilities across different implementations and help identify fundamental model limitations.
Consider incorporating relevant public benchmarks into your evaluation strategy. These might include general capability benchmarks testing reasoning and language understanding, domain-specific benchmarks for specialized applications, tool use benchmarks measuring API interaction quality, or adversarial benchmarks testing robustness.
However, do not rely exclusively on public benchmarks. Research indicates that traditional benchmarks often lack critical aspects, particularly when evaluating performance across multiple dynamic exchanges rather than single-round interactions. Balance standardized testing with custom evaluations addressing your specific requirements.
Validate Dataset Quality Through Meta-Evaluation
Building high-quality evaluation datasets requires evaluating the datasets themselves. Implement systematic checks ensuring datasets meet quality standards before using them to assess agent performance.
Check for dataset balance across relevant dimensions. Ensure adequate coverage of different scenario types. Verify ground truth labels for accuracy and consistency. Validate that test cases remain relevant as requirements evolve. Confirm that dataset size provides statistical reliability.
Perform periodic audits where subject matter experts review random dataset samples. Measure inter-rater reliability on labeling tasks. Track how often test cases reveal actual issues versus false positives. These meta-evaluations surface dataset quality problems before they undermine confidence in results.
Conclusion: Datasets as Living Assets
Building robust evaluation datasets for AI agents requires treating them as living assets that evolve alongside your applications. The techniques outlined in this guide—comprehensive trace logging, balanced curation, expert labeling, production data integration, multi-turn consideration, continuous evolution, clear success criteria, human-in-the-loop workflows, benchmark testing, and quality validation—work together to create evaluation infrastructure that drives meaningful improvement.
Organizations that invest in systematic dataset development gain significant advantages. They catch quality issues before production deployment, measure improvements objectively across iterations, build confidence in agent reliability, and accelerate development through faster feedback cycles.
The key to success lies in starting systematically from day one. Implement comprehensive logging immediately. Begin curating test cases as you develop features. Establish clear quality criteria early. Build human review workflows before you need them urgently. These foundational practices pay dividends throughout the agent lifecycle.
Ready to build robust evaluation datasets with comprehensive tooling? Get started with Maxim to access simulation, evaluation, and observability tools that help teams systematically assess and improve AI agent quality, or schedule a demo to see how leading AI teams build evaluation infrastructure at scale.
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