How to Evaluate AI Systems

Last Tuesday, your on-call engineer got paged at 2 am. A production support agent had selected the wrong tool, yet every dashboard showed successful completions. Customers noticed the errors before your team could trace their source. The failure becomes a leadership problem. Your engineers lose days searching logs, releases slow down, and executives question the return on your AI investment. The demo is cheap — production isn't.

Gartner reports that at least 50% of generative AI projects were abandoned after proof of concept by the end of 2025. Poor data, inadequate controls, rising costs, and unclear business value drove those decisions.

Systematic, end-to-end evals separate a demo that dazzles from an agent you can trust in production. These six steps reduce deployment risk, prove business value, and support regulatory obligations.

What is AI evaluation?

AI evaluation, aka evals, gives you a way to assess a system against performance benchmarks, business requirements, and ethical standards. Unlike traditional software testing, AI evals cover deterministic functions and probabilistic behavior that changes with inputs, context, and model variability.

Traditional machine learning relied on accuracy, precision, and recall measured against definitive ground truth. Generative and agentic systems require assessing output quality, reasoning, safety, user experience, and business impact alongside technical performance.

Evolution of AI evaluation methods

Early AI eval methods focused on classification metrics measured against labeled datasets. More capable systems introduced benchmarks such as General Language Understanding Evaluation (GLUE) and SuperGLUE for natural language understanding. But static benchmarks don't give you the full picture.

Stanford benchmark research finds that AI capabilities are advancing faster than the benchmarks designed to measure them. Training-data contamination can also inflate benchmark scores by an estimated 6-40%.

Modern evals lean on human feedback, model-based judgment, custom metrics, and multidimensional scorecards, and they belong inside development workflows rather than at the final release checkpoint.

How to build an AI evaluation system: Step by step

Step 1: Define clear success criteria with stakeholders

A technically strong model can still fail to create business value. That happens when your engineering team chases accuracy while business stakeholders track return on investment (ROI), reliability, and customer satisfaction, and security stakeholders watch for unsafe actions or data exposure.

Fixing that after development is harder, so start before anyone writes code. Run interviews with engineering, product, operations, security, compliance, and executive sponsors. This matters even more for production agent systems, where failures span multistep decisions rather than one output.

Push stakeholders beyond vague aspirations. Ask them to define measurable outcomes:

• Which business metrics should improve?
• Which customer behaviors indicate success?
• What failure rate would block a release?
• Which actions always require escalation?
• How will regulatory requirements be validated?

Document the answers in an eval framework that names priorities, owners, thresholds, and conflicts between objectives. When priorities compete, weight them explicitly instead of relitigating each release.

Step 2: Build test datasets that mirror production

Your conclusions are only as good as the data behind them. Artificial benchmarks often differ from real traffic, producing models that perform well in development and collapse after release.

The data-quality problem is measurable. Researchers auditing text-to-structured query language (SQL) benchmarks found annotation error rates of 52.8% on BIRD Mini-Dev and 62.8% on Spider 2.0-Snow. Correcting those annotations shifted production-agent leaderboard rankings by up to nine positions.

Your team should include:

• Common production requests and expected workflows
• Ambiguous or incomplete instructions
• Out-of-distribution examples
• Adversarial inputs and prompt attacks
• Tool failures, timeouts, and invalid arguments
• Scenarios near safety or compliance boundaries

The most common mistake is testing only the happy path. Static golden datasets miss failures because production inputs keep changing after the dataset freezes.

Your recommendation agent passes every curated test, then fails when a product sells out between retrieval and checkout. Add stale inventory, tool timeouts, and substitution requests, and let the dataset grow with every production incident.

Step 3: Implement ground truth alternatives

You can't always rely on the definitive correct answers traditional machine learning uses. Generative AI creates a harder problem because several responses may be valid, useful, and safe. Small samples of human judgment or crude proxies such as response length and keyword matching introduce inconsistencies and overlook dimensions such as reasoning coherence.

Consensus methods provide a stronger alternative. A study of large language model (LLM) judges found that heterogeneous judges from different model families aligned better with human judgments than one GPT-4 judge. The panel also cost seven times less.

Model-based judges still need validation. A systematic analysis of roughly 541,000 judgments found strong repeatability alongside severe position bias. Your team should test judge consistency, reverse answer order, compare outputs with expert labels, and review disagreements.

Splunk Agent Observability's Luna evaluation models are purpose-built small language models for production-scale evals. They run at up to 96% lower cost than frontier LLM-as-judge evaluation, with sub-200ms latency. An architecture shared by the three billion and eight billion models lets your team run specialized checks on full production traffic instead of small samples.

Step 4: Select metrics that matter for your use case

Tracking everything creates noise; tracking only technical accuracy hides customer, operational, and business failures.

Choose metrics based on your use case, risk profile, and desired outcomes. A production support agent may need Action Completion, Tool Selection Quality, and Conversation Quality. A developer agent system may require code correctness, reasoning coherence, and safe tool execution.

A practical scorecard can cover seven distinct categories:

• Agentic performance, including action completion, efficiency, tool selection, and flow
• Expression and readability
• Multimodal quality
• Response quality
• Retrieval-augmented generation
• Safety and compliance, including personally identifiable information (PII), toxicity, and prompt injection attacks
• Text-to-SQL performance

Custom metrics translate business requirements into measurable criteria, such as whether a support agent follows refund policy.

Reliability and business outcomes should lead your scorecard, with compliance included where relevant. The European Union (EU) AI Act's transparency obligations took effect on August 2, 2026, requiring systems that interact with individuals to disclose that they're AI and synthetic content to carry machine-readable marking. Violations can carry fines of €15 million or 3% of global annual turnover.

High-risk system obligations under Annex III follow in December 2027, and National Institute of Standards and Technology (NIST) guidance recommends assessing risk-relevant capabilities before deployment and throughout operation, so eval records you create now feed later compliance documentation.

Step 5: Detect patterns and drive continuous improvement

Collecting scores is only the beginning; a graveyard of dashboards drives no decisions. Individual failures rarely reveal the whole problem; a wrong tool call may trace back to prompt design, context handling, tool definitions, or model behavior.

Two production agents. Same input. Completely different outputs. One production support agent verifies account ownership before issuing a refund. The second skips verification because a tool description implies that authentication already occurred. A response-quality score may rate both outputs highly, while a trajectory eval exposes the unsafe path.

Look for clusters rather than isolated incidents. Segment results by task, customer intent, model version, tool sequence, geography, and deployment environment. Then connect each pattern to customer impact, debugging effort, and release risk.

Prompt engineering may clear minor issues; larger problems need dataset augmentation, fine-tuning, tool redesign, or architectural changes, ranked by severity, frequency, customer impact, and effort.

Step 6: Establish automated eval workflows

Point-in-time evals don't stay current as customer behavior, data distributions, tools, prompts, and models change. Manual review creates bottlenecks that push your team to defer testing or cut corners.

Continuous evals belong in your production-agent continuous integration and continuous delivery (CI/CD) lifecycle. Run them when prompts change, tools are added, models are replaced, or datasets are updated. A failed threshold should block a release before the regression reaches customers.

scores = run_agent_evals(candidate_release) 

assert scores["task_success"] >= release_threshold 

assert scores["tool_accuracy"] >= tool_threshold 

assert scores["safety"] >= safety_threshold 

The exact thresholds should come from the success criteria established in step 1. Store the dataset version, prompt version, model configuration, and metric definitions with every run. That record makes regressions reproducible.

Production evals should use the same core metrics, with additional checks for drift and emerging failure patterns. Pair automated gates with agent observability practices so differences between development and production surface early.

Build reliable AI systems and production agents

Effective AI evals connect six practices into one operating cycle: define success with stakeholders, test against realistic data, validate your ground truth alternatives, select metrics tied to outcomes, use production patterns to prioritize improvements, and automate those checks across CI/CD and live traffic. Teams that run the cycle debug faster and can show leadership where the AI investment is paying off.

Splunk is the intelligence layer for trusted agentic operations, extending end-to-end visibility from digital systems into the agents acting across them. Splunk Agent Observability supports that work with four capabilities:

• Agent observability: Trace decision paths and tool calls so your team can locate failures faster.
• Luna evaluation models: Run accurate, low-latency evals across production traffic without frontier LLM-as-judge costs.
• Agentic evaluation metrics: Measure action completion, tool selection, reasoning, and workflow quality.
• Signals failure detection: Surface recurring and previously unknown failure patterns before they spread.

Frequently asked questions