Introducing AI Evaluation: Close the Loop Across the Full AI Lifecycle

Enterprise engineering teams are rushing to deploy autonomous agents and RAG architectures, but moving generative AI from pilot to production exposes a critical visibility gap in traditional software monitoring. While point solutions isolate single-LLM calls in a sandbox, enterprise AI applications operate as complex, probabilistic transactions across distributed systems. Observing AI in a silo creates dangerous blind spots around upstream and downstream operational impacts while widening the gap between AI developers and production engineering. Traditional monitoring measures throughput and latency, but misses semantic failures—leaving teams blind to hallucinations, PII leaks, and unexpected model drift.

New Relic AI Evaluation closes this trust gap by integrating automated qualitative guardrails directly into your existing observability workflow. Powered by an asynchronous LLM-as-a-judge service, it automatically scores live prompts and completions against strict safety and accuracy parameters, attaching those quality metrics directly to your distributed traces. By mapping semantic response quality straight to underlying software spans and infrastructure costs, leaders gain the real-time control required to protect brand reputation, optimize LLM spend, and confidently scale AI applications in production.

Scaling Enterprise GenAI Safely

Built as a platform capability within New Relic AI Observability, AI Evaluation transforms how engineering teams manage non-deterministic applications by expanding standard performance tracking into semantic and qualitative observability. Instead of treating LLM responses as uninspectable black boxes, teams can now automatically measure the accuracy, safety, and relevance of their AI inputs and completions alongside core application telemetry.

Powered by specialized evaluation models, the service evaluates sampled prompt payloads and model completions against predefined quality and security parameters. Each evaluated interaction receives a normalized score from 0 to 1, a clear pass/fail status based on customizable thresholds, and an explanatory reasoning string, ensuring your team receives actionable feedback on silent failures like hallucinations or data leaks.

Unlike standalone point solutions that isolate evaluation data, AI Evaluation is built natively within the New Relic platform. Quality scores, failure flags, and judgement rationale are automatically returned to the platform and attached as attributes directly to your original distributed traces. This seamless integration connects probabilistic quality scores straight to software root causes, allowing you to navigate from a low-quality response score directly to the specific prompt span, vector database retrieval call, or backend service that caused the issue within a single, unified view.

Optimize Your AI Spending

As enterprise AI deployment expands, engineering leaders and platform teams face the growing challenge of balancing exceptional user experiences against spiraling model compute costs. AI Evaluation bridges performance telemetry with financial governance by connecting qualitative response scores directly to underlying infrastructure consumption. This contextual visibility allows teams to evaluate whether expensive, high-parameter models deliver superior accuracy and relevance compared to faster, lower-cost alternatives. Armed with clear quality-to-cost correlations, organizations can optimize their model selection and swap out underperforming LLMs to lower token spend without compromising answer quality or customer trust.

To prevent evaluation overhead from straining resources, AI Evaluation incorporates intelligent, highly configurable sampling. Platform operators can establish granular sampling rules on live traffic, such as running toxicity or data leakage checks on a sampling of 10 percent of transactions, to maintain continuous safety oversight without evaluating every single interaction. This asynchronous processing delivers high-integrity compliance monitoring and vulnerability detection while keeping operational expenses strictly controlled. By tailoring sampling strategies to specific application workloads, enterprise teams maintain robust quality guardrails while optimizing the total cost of ownership for their observability stack.

Accelerating Time-to-Market without Compromising Quality

Safely transitioning generative AI applications from experimental pilots to production requires continuous oversight that extends beyond traditional technical metrics. AI Evaluation delivers an end-to-end operational framework that automatically assesses LLM interactions for accuracy, safety, and relevance while mapping those insights directly to your underlying application telemetry. By combining real-time security guardrails, architectural failure isolation, and native distributed trace integration, this capability provides engineering and business leaders with the control needed to protect brand reputation, minimize compliance risks, and accelerate time-to-market.

Real-Time Security and Compliance Guardrails

AI Evaluation provides out-of-the-box evaluators, with no setup or configuration necessary. These safety checks can be embedded directly into live application traffic to protect brand safety and maintain regulatory compliance. By automatically evaluating sampled prompt inputs and model completions, the system flags security risks and compliance threats before they cause reputational or legal harm.

• Prompt Injection and Jailbreak Defense: Identifies adversarial user instructions and attempts to bypass safety guardrails to restrict unauthorized content generation.
• Data Leakage Mitigation: Scans incoming payloads and model completions for sensitive data, automatically flagging personally identifiable information (PII).
• Toxicity and Bias Screening: Automatically detects harmful, offensive, or abusive language, as well as unfair or prejudiced treatment based on protected attributes.

RAG Architecture and Agent Performance Isolation

When an autonomous agent or Retrieval-Augmented Generation (RAG) system delivers an inaccurate answer, identifying the failing component is critical. AI Evaluation isolates failure modes by evaluating quality across specialized retrieval and generation metrics:

• Faithfulness (Hallucination Tracking): Measures whether generated responses are factually consistent with the provided retrieval context.
• Answer and Contextual Relevancy: Measures how well retrieved information matches the user query and whether the final response directly addresses the prompt.

Direct Distributed Trace Integration

Unlike some standalone LLM observability tools that store evaluation scores in disconnected silos, AI Evaluation feeds judgement results directly back into your core telemetry stream.

• Transaction-Level Context: Instead of evaluating isolated LLM calls, AI Evaluation looks holistically across the entire transaction, linking qualitative response scores directly to upstream prompts, vector database retrievals, and downstream compute consumption in a single view.
• Attribute Enrichment: Normalized quality scores, pass/fail statuses, and reasoning strings are ingested back into the platform and attached directly as attributes to the original distributed trace.
• Unified Stack Visibility: Engineers can view prompt payloads, judgement rationale, and cross-stack application behavior on a single screen.
• Accelerated Troubleshooting: When an interaction fails a quality threshold, responders can navigate directly from the evaluation alert to the precise execution span, vector database retrieval call, or backend service responsible for the failure.

Strategic ROI Across the Enterprise

By expanding observability beyond traditional system metrics into semantic evaluation, New Relic AI Evaluation transforms stalled AI prototypes into secure, production-ready enterprise services. Automating quality and safety guardrails eliminates the manual log reviews that create severe deployment bottlenecks, allowing organizations to ship generative AI applications faster while insulating their brand from reputational and compliance risks. This capability delivers targeted value across three key stakeholder roles:

• AI/ML & LLMOps Engineers: Practitioners get real-time evaluation scores tied directly to distributed traces, replacing unscalable log spreadsheets and manual reviews. When fine-tuning system prompts or optimizing RAG retrieval logic, engineers can instantly verify whether modifications improve answer relevance or inadvertently trigger regressions like factual hallucinations.
• Platform Engineers, SREs & DevOps Leads: Platform leads can align system reliability and compute performance with financial governance. By correlating qualitative response metrics with underlying token costs, operators can evaluate whether expensive models justify their compute spend and set intelligent sampling rules to maintain platform performance without inflating infrastructure budgets.
• Security & Compliance Officers: Compliance leaders receive the continuous, automated oversight required to clear autonomous bots and public-facing AI agents for production. Automated scanning for prompt injection attacks, jailbreaks, PII leaks, and toxic outputs provides proactive risk mitigation, ensuring AI applications comply with strict data privacy standards and corporate brand guidelines.

Accelerate Your AI Deployments Today

Deploying AI Evaluation within your existing telemetry workflow requires zero complex setup or custom code rewriting. Because native APM agents automatically capture prompt input and model output payloads as part of standard trace instrumentation, your team can begin capturing qualitative telemetry immediately. Simply configure your preferred flagging criteria, establish your sampling rules, and watch real-time quality scores automatically enrich your distributed traces.

Don't let silent failures, data leakage risks, or prompt regressions keep your generative AI applications trapped in experimental pilots. Request a live demonstration today to see how New Relic AI Evaluation brings real-time safety guardrails to your LLM architecture, or explore our technical documentation to start evaluating your production AI workloads now.

Please join us at New Relic Now 2026 to learn more and sign up to participate in the public preview.