Artificial intelligence (AI) is fast becoming the defining technology of our time. A new UN Trade and Development (UNCTAD) report projects the global AI market will soar from $189 billion in 2023 to $4.8 trillion by 2033 – a 25-fold increase in just a decade.

But with this explosive growth comes heightened risk. 70% of AI initiatives stall once prototypes hit production, mainly because hidden errors stay invisible until customers notice. When that happens, you scramble through logs, guess at root causes, and hope the next deploy won't break something else.

Successful AI deployment demands more than sophisticated models; it requires systematic, end-to-end evaluation throughout the development lifecycle. 

This guide presents a practical, step-by-step approach for AI evaluation, distilling best practices from production-scale deployments. Follow these methods to reduce deployment risk, meet emerging regulations like the EU AI Act, and demonstrate concrete ROI to executives.

We recently explored this topic on our Chain of Thought podcast, where industry experts shared practical insights and real-world implementation strategies:

What is AI evaluation?

AI evaluation is the systematic assessment of artificial intelligence systems against performance benchmarks, business requirements, and ethical standards. Unlike traditional software testing, AI evaluation must address both deterministic functions and probabilistic behaviors that vary based on inputs, context, and even random factors.

Traditional machine learning relied primarily on quantitative metrics like accuracy, precision, and recall, measured against definitive ground truth.

Modern AI – especially generative systems – demands a multidimensional approach examining not just technical performance but also output quality, alignment with human values, and business impact.

Evolution of AI evaluation methods

AI evaluation has transformed dramatically alongside AI capabilities. Early approaches focused on simple classification metrics against labeled datasets. As AI systems grew more complex, evaluation expanded to include specialized benchmarks like GLUE and SuperGLUE for natural language understanding.

The emergence of large language models introduced new challenges, as these systems produce varied outputs that resist simple true/false assessment. Evaluation methods evolved to incorporate human feedback, model-based judgment, and multi-dimensional frameworks assessing factors from factual accuracy to ethical alignment.

Today's evaluation approaches increasingly integrate into development workflows rather than serving as final checkpoints.

This shift from post-hoc verification to continuous quality assurance reflects the understanding that robust AI systems require ongoing evaluation throughout their lifecycle – from initial design through deployment and beyond.

These evaluation advancements set the foundation for the structured step-by-step approach detailed in the following steps, guiding teams through implementing comprehensive AI evaluation frameworks.

AI evaluation step #1: Define clear success criteria with stakeholders

You've likely experienced the frustration of a technically sound AI model that fails to deliver business value. This disconnect often stems from misalignment between technical metrics and stakeholder expectations.

Engineering teams optimize for model accuracy while business leaders focus on ROI, regulatory compliance, and user satisfaction – creating a fundamental misalignment that dooms projects from the start.

Many organizations make the critical mistake of beginning evaluation after development, when it's too late to address fundamental design issues. Instead, successful evaluation begins before a single line of code is written, with structured stakeholder interviews capturing diverse perspectives.

Your interviews should include technical teams, business units, compliance officers, and executive sponsors to understand what "good" looks like from every angle.

Rather than vague aspirations, push stakeholders to articulate specific, measurable outcomes:

• What business metrics should improve?

• What user behaviors indicate success?

• How will regulatory requirements be validated?

Document these criteria in a unified evaluation framework that clarifies priorities and identifies potential conflicts between objectives.

When competing priorities emerge, techniques like the Analytic Hierarchy Process can help quantify relative importance. This structured approach transforms subjective preferences into weighted criteria, creating transparency around inevitable trade-offs.

AI evaluation step #2: Build comprehensive test datasets

Your AI evaluation is only as good as the data powering it. Many enterprises fail by testing on artificial benchmarks that bear little resemblance to real-world conditions, resulting in models that perform brilliantly in the lab but collapse in production.

The more complex your AI system, the more critical diverse, representative test data becomes.

Building effective test datasets requires balancing several considerations. You need sufficient volume to ensure statistical validity, appropriate diversity to test across different conditions, and careful curation to avoid introducing bias.

Most organizations underestimate how much their training data differs from production environments, particularly when user behavior evolves over time.

The most common mistake is focusing exclusively on "happy path" scenarios while neglecting edge cases that often cause catastrophic failures. Your test datasets should deliberately include challenging inputs: ambiguous requests, out-of-distribution examples, adversarial inputs designed to confuse the model, and scenarios representing compliance boundaries.

For enterprises operating in regulated industries, test datasets must also reflect specific compliance requirements. Financial services firms need scenario testing for fair lending practices, while healthcare organizations must validate HIPAA compliance across diverse patient populations.

Galileo's Experiment functionality addresses these challenges through a systematic approach to test dataset management. This feature enables you to build, version, and deploy test datasets that mirror production conditions.

Rather than static collections, these datasets become living resources that evolve alongside your applications, with automatic versioning ensuring reproducible evaluations across development cycles.

AI evaluation step #3: Implement robust ground truth alternatives

When evaluating traditional ML models, you compare outputs against definitive correct answers. But generative AI presents a fundamental challenge: there's often no single "right" response to compare against.

How do you evaluate quality when ground truth is ambiguous or nonexistent? This challenge derails many evaluation efforts before they begin.

Organizations typically respond with makeshift approaches – relying on small samples of human judgments or using crude proxies like length or keyword matching. These methods prove inadequate at scale, introducing inconsistencies and failing to capture nuanced quality dimensions like reasoning or coherence.

A more effective approach leverages consensus methods, gathering judgments from multiple models or evaluators to approximate ground truth. This technique reduces individual biases but requires careful orchestration across evaluation sources.

Luna-2, Galileo's suite of Small Language Models (SLMs), delivers purpose-built evaluation at a fraction of the cost of traditional LLM-based approaches. With evaluation latency under 200ms and costs around $0.02 per million tokens (97% cheaper than GPT-4), Luna-2 makes comprehensive evaluation economically feasible even for large-scale enterprise deployments. 

The multi-headed architecture enables hundreds of specialized metrics on shared infrastructure, providing ground truth alternatives across diverse evaluation dimensions.

AI evaluation step #4: Select metrics that matter for your use case

You face an overwhelming array of potential metrics when evaluating AI systems. Many teams make the critical mistake of tracking too many metrics without clear prioritization, creating noise that obscures meaningful insights.

Others focus exclusively on technical metrics like accuracy while neglecting business impact or user experience measures. Both approaches lead to misguided optimization.

Effective evaluation requires selecting metrics that align with your specific use case, risk profile, and business objectives. Technical performance forms just one dimension of a comprehensive framework that should also address relevance, safety, efficiency, and business impact.

Regulatory compliance adds another layer of complexity, particularly in industries like finance, healthcare, and insurance. Emerging frameworks like the EU AI Act demand specific evaluation approaches for high-risk applications, while sector-specific regulations impose additional requirements.

Galileo's comprehensive metrics address these challenges with purpose-built evaluators across five key categories:

• Agentic flow

• Expression/readability

• Model confidence

• Response quality

• Safety/compliance

Rather than generic metrics, these evaluators target specific AI behaviors. Further custom metrics capabilities let you define evaluation criteria using natural language descriptions, translating business requirements directly into measurable indicators.

AI evaluation step #5: Detect patterns and implement continuous improvement

You've collected evaluation data – now what? Many organizations struggle with the "metrics graveyard" problem: they gather volumes of evaluation results but fail to translate them into actionable improvements.

They might react to isolated incidents while missing systematic patterns, or identify issues without establishing clear processes for addressing them.

The root causes of AI failures typically extend beyond surface-level symptoms. When LLMs select incorrect tools or language models generate hallucinations, the underlying issues often involve complex interactions between prompt design, context handling, and model behavior. 

Once patterns emerge, you need a structured approach to improvement. For minor issues, prompt engineering might suffice. More significant problems might require dataset augmentation, fine-tuning, or architectural changes.

However, without a prioritized improvement roadmap balancing quick wins with fundamental solutions, teams waste resources on superficial fixes.

Galileo's Insights Engine transforms evaluation from reactive monitoring to proactive improvement. This automated analysis system identifies failure patterns from logs and surfaces them with actionable root cause analyses.

Instead of spending hours scrolling through traces, your team receives clear, prioritized insights about tool errors, planning breakdowns, and other systematic issues.

AI evaluation step #6: Design effective guardrails for production safety

Your AI system performed well in testing, but what happens when it encounters unexpected inputs in production? Without proper guardrails, even well-designed systems can produce harmful, biased, or nonsensical outputs when facing real-world conditions.

Many organizations discover this vulnerability too late, after customer-facing failures damage trust and reputation.

Traditional approaches to AI safety rely heavily on human review, which doesn't scale with growing deployment volume. Other organizations implement crude filtering mechanisms that catch obvious issues but miss subtle problems like hallucinated information presented confidently.

Effective guardrails must balance restrictiveness with utility. Overly strict constraints limit functionality and frustrate users, while insufficient boundaries create safety risks. This calibration must be contextual – a customer service chatbot requires different protection than a medical advisor or financial system.

Many teams struggle to find this balance, either compromising safety or hobbling their AI's capabilities.

Galileo's runtime protection provides the industry's only runtime intervention capability with deterministic override/passthrough actions. Unlike passive monitoring tools that identify problems after they occur, runtime protection intercepts risky outputs before they reach users. 

This real-time guardrail system blocks hallucinations, detects PII exposure, and prevents prompt injection attacks without adding significant latency. Each intervention generates comprehensive audit trails, providing documentation essential for regulatory compliance and continuous improvement.

AI evaluation step #7: Establish automated evaluation workflows

Point-in-time evaluations quickly become outdated as data distributions shift, user behavior evolves, and new edge cases emerge. Many organizations evaluate extensively during development but neglect ongoing monitoring, only discovering performance degradation after customer complaints or business impact.

By then, fixing issues becomes exponentially more expensive and damaging.

Manual evaluation processes create bottlenecks that slow innovation. Teams defer updates because evaluation takes too long, allowing competitors to move faster. Other organizations cut corners on evaluation to meet deadlines, introducing unnecessary risks. Neither approach proves sustainable for enterprise AI deployments.

The gap between development and production environments compounds these challenges. Systems evaluated in controlled settings behave differently under real-world conditions with unpredictable inputs, varying latency requirements, and integration complexities.

Without continuous evaluation spanning both environments, these divergences remain invisible until they cause failures.

Effective continuous evaluation requires integrating testing into your CI/CD pipelines, automating evaluation whenever code changes or models are retrained. Data drift detection systems should alert you when input distributions change significantly, triggering targeted evaluations.

Evaluate your AI systems and agents with Galileo

Implementing comprehensive AI evaluation creates a virtuous cycle – catching issues early reduces development costs, building stakeholder trust enables more ambitious projects, and documenting system performance satisfies regulatory requirements.

Galileo provides the infrastructure to make this systematic approach practical for enterprise teams:

• Complete evaluation infrastructure for the entire AI lifecycle: Galileo supports every phase from development through production with specialized tools for each stage. Our platform enables systematic testing during development, comprehensive evaluation before deployment, and continuous monitoring in production.

• Purpose-built metrics for complex AI systems: With specialized evaluators for agentic systems, RAG applications, and conversational AI, Galileo provides insights that traditional monitoring tools miss.

• Enterprise-grade security and deployment flexibility: Whether you require on-premise deployment for sensitive applications or cloud-based monitoring for distributed systems, Galileo's SOC 2 compliance and enterprise-grade security ensure evaluation doesn't compromise system protection.

• Scalable architecture proven at enterprise volume: Processing over 20 million traces daily while supporting 50,000+ live agents on a single platform, Galileo handles enterprise-scale evaluation without performance degradation.

• Integration with your existing AI stack: Galileo works with any LLM provider, framework, or cloud, fitting seamlessly into your current architecture rather than requiring disruptive changes.

• Intelligent failure detection: Galileo’s Insights Engine automatically clusters similar failures, surfaces root-cause patterns, and recommends fixes, reducing debugging time while building institutional knowledge

• Human-in-the-loop optimization: Galileo's Continuous Learning via Human Feedback (CLHF) transforms expert reviews into reusable evaluators, accelerating iteration while maintaining quality standards

Get started with Galileo today to transform how your enterprise evaluates, improves, and governs AI systems throughout their lifecycle.

The best AI agents are currently failing about 70 percent of the tasks assigned to them, as investments are expected to drop off a cliff. As autonomous agents increasingly handle sensitive tasks from customer service to financial operations, the stakes for reliability have never been higher.

If you want reliable AI agents, you need structured evaluation methodologies that address emergent risks before deployment, not after public failures damage customer trust and brand reputation, and that’s exactly what this guide delivers.

What is AI agent evaluation?

AI agent evaluation is the systematic process of assessing how effectively autonomous AI systems perform complex, multi-step tasks involving decision-making, tool use, and real-world interactions.

Unlike evaluating simple language models, agent evaluation focuses on the entire execution path of autonomous systems that take actions on behalf of users or organizations. Proper evaluation encompasses:

Key aspects of AI agent evaluation

A comprehensive evaluation framework must address multiple dimensions that reflect the complexity of agent systems:

• Accuracy and effectiveness: How well agents accomplish their designated tasks

• Efficiency and resource utilization: The computational and time costs of agent operation

• Tool selection and usage: Appropriateness of tool choices and parameter values

• Decision path analysis: Clarity and coherence of agent reasoning steps

• Context retention: Ability to maintain a consistent understanding across conversations

• Failure recovery: How agents respond when encountering errors or unexpected inputs

• Safety and compliance: Adherence to ethical guidelines and regulatory requirements

Agent evaluation step #1: Define clear success criteria before building

You've likely experienced the frustration of launching an agent only to discover stakeholders had different expectations for what "success" meant. Many teams jump straight to implementation without establishing clear evaluation criteria, leading to missed requirements and post-deployment crises.

This approach creates a dangerous pattern: development proceeds based on subjective impressions rather than measurable outcomes, and you end up with agents that perform impressively in demos but fail under real-world conditions.

Rather than starting with prompt engineering, begin by defining quantifiable metrics that align with business objectives:

• What specific tasks must your agent perform?

• How will you measure success beyond simple completion?

For example, a customer service agent needs metrics for both task completion (did it resolve the issue?) and interaction quality (was the experience positive?).

Agent evaluation step #2: Build a diverse testing dataset

The most common failure in agent evaluation stems from testing with homogeneous, idealized examples that don't reflect real-world complexity. Your perfectly functioning agent can suddenly fail when deployed because users ask questions differently than your test cases anticipated, use ambiguous language, or change their minds mid-conversation.

Without diverse test cases, you create a dangerous illusion of reliability.

Effective dataset creation requires careful consideration of edge cases, unusual requests, and the full spectrum of user behaviors. Start by collecting actual user interactions from similar systems, then augment with synthetic examples designed to challenge your agent's capabilities. 

Include adversarial examples, ambiguous instructions, and scenarios where requirements change mid-conversation.

You need a dataset collection to help you build comprehensive test suites that support systematic agent evaluation. This enables structured organization and versioning of test cases with automatic versioning for reproducibility.

You can also implement dynamic prompt templating with mustache syntax and nested field access to create variations efficiently. Most importantly, a dataset collection helps you easily measure agent performance across diverse scenarios, identifying patterns of failure that might otherwise remain hidden until production deployment.

Agent evaluation step #3: Measure end-to-end task completion

When evaluating AI agents, many teams make a critical mistake: they focus on individual responses rather than complete task journeys. Your agent might produce coherent, helpful replies at each step while still failing to accomplish the user's overall goal.

This disconnect occurs because traditional evaluation approaches don't capture how well agents maintain context across turns, adapt to changing requirements, or ultimately deliver what users need.

End-to-end evaluation requires tracking complete conversation arcs from initial request to final resolution:

• Did the agent fully understand and accomplish the user's intent?

• Did it maintain context across multiple turns?

• Could it adapt when requirements shifted?

These questions require session-level metrics rather than turn-by-turn assessment.

Galileo's session tracking capabilities provide you with comprehensive visibility into complete user interactions. For a deeper analysis, leverage spans, which are the fundamental building blocks of traces, representing discrete operations or units of work.

A span captures a single step in your application’s workflow, such as an LLM call, a document retrieval operation, a tool execution, or any other distinct process.

Unlike basic trace views that treat each interaction as isolated, session-level intelligence helps you understand the complete user journey and identify where breakdowns occur in complex multi-step workflows.

Agent evaluation step #4: Assess tool selection and usage quality

The heart of agent intelligence lies in their ability to select and use the right tools at the right time. Yet many evaluation approaches overlook this critical capability, focusing instead on the quality of natural language responses.

Your agent might produce beautifully written explanations while making fundamentally wrong decisions about which API to call or what parameters to use. This disconnect creates a dangerous gap in your evaluation strategy, leaving you blind to the most common failure modes in production.

Tool evaluation requires specialized metrics that assess both the selection decision (did the agent choose the appropriate tool?) and parameter quality (were the parameters correct and well-formatted?).

You need complete visibility into the agent's decision process, not just the final output, to understand why it might choose a web search when it should access a database, or why it passes malformed parameters to APIs.

Platforms like Galileo enable you to establish custom metrics designed specifically for agentic workflows. With Galileo's metrics configuration system, you can implement proprietary metrics such as:

• Tool selection quality, which evaluates if agents choose the correct tools with appropriate parameters

• Action completion, which measures if agents fully accomplish every user goal across multi-turn conversations.

Unlike generic observability tools, Galileo provides agent-specific metrics out of the box while allowing custom configurations tailored to your business domain. With comprehensive observability into tool usage patterns, you can rapidly pinpoint and fix the most common sources of agent failures before they impact users.

Agent evaluation step #5: Implement runtime safeguards and guardrails

Most enterprises discover too late that evaluation alone isn't enough, you need active protection against agent failures in production. Without runtime safeguards, your carefully tested agent can still produce harmful outputs, leak sensitive information, or make costly mistakes when confronted with unexpected inputs.

Even with a thorough pre-deployment evaluation, the combinatorial explosion of possible inputs means some edge cases will inevitably slip through.

Production protection requires implementing guardrails that can detect and prevent harmful outputs before they reach users. These safeguards must operate with minimal latency impact while providing deterministic control over agent actions. The goal isn't just to identify problems but to actively stop them from affecting users or systems.

Galileo’s runtime protection provides industry-leading runtime protection capabilities that intercept potentially harmful outputs before execution.

Unlike passive monitoring tools, Galileo implements real-time guardrails for blocking hallucinations, detecting PII exposure, and preventing prompt injection attacks with deterministic override/passthrough actions.

Each intervention is logged with comprehensive audit trails for compliance requirements. By embedding modern protective measures into your agent deployment pipeline, you create a last line of defense that catches problems even when they evade pre-deployment testing.

Agent evaluation step #6: Automate evaluation at scale

Manual evaluation quickly becomes unsustainable as agent complexity and interaction volume grow. Many teams start with thorough evaluation processes but gradually scale back as the effort becomes overwhelming, creating an increasing risk of undetected failures.

This efficiency gap forces an impossible choice between comprehensive evaluation and development velocity.

Scaling evaluation requires automation that can process thousands of test cases efficiently while providing actionable insights. Manual review simply can't keep pace with the exponential complexity of agent behaviors, especially in multi-agent systems where interactions between components create a combinatorial explosion of possible execution paths.

Enter proprietary purpose-built small language models like Luna-2.

With Luna-2, you can address the evaluation challenge by getting ultra-low-cost, high-accuracy AI evaluations through specialized Small Language Models. With evaluation costs at just $0.02 per million tokens (97% cheaper than GPT-4 alternatives) and sub-200ms latency, Luna-2 makes comprehensive evaluation economically viable even at enterprise scale.

Luna-2's multi-headed architecture enables running hundreds of metrics on shared infrastructure, while Continuous Learning via Human Feedback (CLHF) allows rapid customization with just 2-5 examples.

By integrating automated evaluation into your CI/CD pipeline, you create quality gates that prevent regressions while maintaining development velocity.

Agent evaluation step #7: Establish continuous monitoring and improvement cycles

Even the most rigorous pre-deployment evaluation can't predict how agents will perform against the full spectrum of real-world inputs and edge cases. Many teams discover critical failures only after they've impacted users, creating crisis response cycles that damage trust and disrupt development plans.

Without systematic monitoring and improvement processes, agent quality gradually degrades as environments change and new failure modes emerge.

Effective lifecycle management requires establishing feedback loops that continuously monitor production performance and feed insights back into development. You need to capture both successful and failed interactions, identify emerging patterns, and prioritize improvements based on real-world impact rather than theoretical concerns.

Galileo's Insights Engine automatically identifies failure patterns from logs, reducing debugging time from hours to minutes with actionable root cause analysis. For ongoing improvement, the experimentation framework enables systematic A/B testing to validate changes before full deployment.

This closed-loop approach ensures your agents continuously improve based on real-world performance data, maintaining reliability even as usage patterns and requirements evolve.

Ship reliable AI agents with Galileo

The journey to reliable AI agents requires systematic evaluation across the entire development lifecycle. With the right framework and tools, you can confidently deploy agents that deliver consistent value while avoiding costly failures.

Galileo's Agent Observability Platform provides the comprehensive capabilities you need:

• Automated quality guardrails in CI/CD: Galileo integrates directly into your development workflow, running comprehensive evaluations on every code change and blocking releases that fail quality thresholds

• Multi-dimensional response evaluation: With Galileo's Luna-2 small language models, you can assess every output across dozens of quality dimensions—correctness, toxicity, bias, adherence—at 97% lower cost than traditional LLM-based evaluation approaches

• Real-time runtime protection: Galileo's Agent Protect scans every prompt and response in production, blocking harmful outputs before they reach users while maintaining detailed compliance logs for audit requirements

• Intelligent failure detection: Galileo’s Insights Engine automatically clusters similar failures, surfaces root-cause patterns, and recommends fixes, reducing debugging time while building institutional knowledge

• Human-in-the-loop optimization: Galileo's Continuous Learning via Human Feedback (CLHF) transforms expert reviews into reusable evaluators, accelerating iteration while maintaining quality standards

Get started with Galileo today and discover how comprehensive observability can elevate your agent development and achieve reliable AI systems that users trust.