Top 10 AI Evaluation Tools for Enterprise AI Engineering Teams

You've probably seen the screenshots: in July 2025, Replit's coding agent deleted a live production database holding records on more than 1,200 executives despite an active code freeze, then told the user a rollback wouldn't work. He recovered the data manually. Google's AI Overviews telling users to mix glue into pizza sauce put the same failure mode in front of a mass audience. Failures like these are hard to catch in advance, in part because generative AI output has no single "right" answer to compare against.

Precision and F1 do fine on deterministic classifiers; neither can score a poem or an agent's choice of tool. Without ground-truth labels, even well-funded teams ship confident failures. And the stakes keep rising as deployment barriers fall: Gartner's survey of 782 infrastructure and operations leaders found that only 28% of AI use cases fully succeed and meet ROI expectations, while 20% fail outright. Gartner also predicts over 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value, or inadequate risk controls.

These 10 AI evaluation tools can prevent production failures, and each carries trade-offs that decide which one fits a given team's needs.

What is an AI evaluation tool?

An AI evaluation tool scores the outputs and behavior of LLM applications and agents against criteria such as correctness, groundedness, safety, and tool use. Most combine LLM-as-a-judge evaluators, code-based evaluators, and human review, and run them in development as regression tests and in production on live traces. Building and maintaining those evals is often called eval engineering.

AI evaluation tool #1: Splunk Agent Observability

When you're shipping autonomous agents, you need to score outputs without ground truth, follow failures across tool calls, and keep production coverage affordable. Splunk Agent Observability is an observability, evaluation, and guardrail platform for GenAI and agentic applications, available in Splunk Observability Cloud or on-premises.

The evaluation engine runs on Splunk Luna evaluators, purpose-built small language model evaluators in 3B and 8B sizes. Per the Luna-2 research paper, Luna-2 3B reaches F1 0.95 on context adherence, comparable to ChainPoll with GPT-4.1 (F1 0.96), and Luna-2 cuts inference cost by more than 80x and latency by more than 20x compared with LLM-based evaluators. The Luna Evaluation Models page puts the savings at up to 98% versus LLM-as-a-judge.

The platform ships 30 prepackaged evaluators, including nine agentic evaluators such as Action Completion and Tool Selection Quality, plus RAG, response quality, safety (PII, CPNI, PHI, prompt injection, bias, toxicity), and text-to-SQL groups. Autotune tunes LLM-as-a-judge evaluators to your domain: from about five corrected examples, Splunk reports accuracy around 95%, and it shows exactly what changed in the evaluator prompt.

Signals analyzes production traces to surface unknown unknowns such as security leaks, policy drift, and cascading failures. It separates new failure modes from known ones and generates an LLM-as-a-judge evaluator from any signal in one click.

The Python SDK (splunk-ao) integrates natively with LangChain and LangGraph, CrewAI, and the OpenAI Agents SDK. Microsoft Agent Framework, Strands Agents, Vercel AI SDK, and Mastra connect through OpenTelemetry. Splunk Luna evaluators are an Enterprise-tier capability, and teams committed to open-source orchestration may prefer to keep their existing stack.

AI evaluation tool #2: MLflow

When an agent moves from experiment to production, keeping models, prompts, datasets, traces, and evaluation results connected becomes the job. MLflow grew out of ML experiment tracking and now reaches into GenAI evaluation. Put its LLM-as-a-judge coverage for factuality, groundedness, and retrieval relevance on your test plan, along with whether tracing follows prompts through tool calls.

The adoption decision comes down to unified lifecycle management: whether you can build eval datasets from production traces and keep lineage across models, prompts, and results alongside traditional experiment tracking. Advanced prompt engineering and multi-agent workflows may still need custom scaffolding.

AI evaluation tool #3: Weights & Biases Weave (part of CoreWeave)

W&B Weave, from Weights & Biases (acquired by CoreWeave in 2025), is an observability and evaluation platform for GenAI applications and agents. CoreWeave now also offers Agent Lens alongside Weave for analyzing production agent traces. Test Weave's automated LLM-as-a-judge scoring, hallucination detection, custom scorers, and real-time tracing against your current requirements.

The practical question is how its prompt engineering, automated testing, and production monitoring workflows sit alongside what your developers already do. Ease of use can trade against customization for highly specialized criteria or multi-agent assessment.

AI evaluation tool #4: Google Gemini Enterprise Agent Platform

Google's Gen AI evaluation service, now part of Gemini Enterprise Agent Platform (formerly Vertex AI), is designed to assess generative models against custom criteria. Map its current support for Google's foundation models and third-party LLMs, model benchmarking, RAG architectures, quality assessment workflows, serving, monitoring, and governance. Third-party models connect through LiteLLM or bring-your-own responses rather than a native connector, so weigh the lock-in against how much of your stack already runs there.

AI evaluation tool #5: Langfuse (part of ClickHouse)

Langfuse is built for visibility into LLM interactions, prompt engineering workflows, and user behavior. Put cost tracking, latency monitoring, session analysis, LLM-as-a-judge templates for hallucination detection, context relevance, and toxicity, plus human annotations and custom scoring through APIs on your requirements list. ClickHouse acquired Langfuse in January 2026, and its core remains open source under the MIT license, so you can read and modify the evaluation layer.

Self-hosting costs someone's time, so settle who owns the infrastructure, the upgrade cadence, and trace retention before you commit. It may work best as part of a broader agent observability stack.

AI evaluation tool #6: Phoenix (Arize AI, part of Dynatrace)

Phoenix is Arize's observability and evaluation platform for LLM applications, published under the Elastic License 2.0. Dynatrace, which completed its acquisition of Arize in October 2026, says it will keep supporting Phoenix. Prioritize its tracing, evaluations, datasets, experiments, and prompt playground, including visibility into RAG pipelines, agent interactions, and multi-step reasoning. Its LLM-as-a-judge evaluators for faithfulness and hallucination score outputs without reference answers; teams that need runtime guardrails will still pair it with a separate enforcement layer.

AI evaluation tool #7: Braintrust

Braintrust is an eval-first platform that brings prompt engineering, systematic testing, and production tracking together. Test its evals scored by LLMs, code, or human reviewers; GitHub Actions integration for pull-request evals and regression checks; asynchronous online scoring on production logs; the Loop agent, which investigates logs and experiments; and Patterns, in public preview, which surfaces recurring issues. It runs as a managed cloud service, with hybrid deployment on the Enterprise plan, so confirm data-residency fit before you commit.

AI evaluation tool #8: LangSmith

LangSmith is LangChain's tracing, evaluation, and dataset-management platform. Tight integration can simplify setup when your agents already use LangChain or LangGraph, but it doesn't require either: its SDKs work with other frameworks, and it accepts OpenTelemetry traces. Test its current support for complex agent workflows, RAG systems, automated evaluation metrics, and human feedback collection, and decide how much of your workflow you want inside the LangChain ecosystem.

AI evaluation tool #9: Opik by Comet

When an agent chooses the wrong tool or takes a bad path through a multi-step task, response-level scoring won't explain enough. Opik, from Comet, is an open-source (Apache 2.0) evaluation platform aimed at agent reliability.

Focus your test plan on the current frameworks for assessing multi-step reasoning, tool usage, and decision-making quality across complex agent interactions, plus automated metrics, tracing, monitoring dashboards, and prompt improvement. Measure integration effort for your framework rather than assuming a quick setup, and make access control and compliance tooling clear your enterprise bar.

AI evaluation tool #10: Confident AI (DeepEval)

When evaluation needs to run like software testing, the key question is which evals work without labels and which depend on a known answer. DeepEval, an open-source (Apache 2.0) framework, is oriented around response-quality assessment; its unit testing workflow and automated scoring are the pieces to test first, with human annotation handled in the Confident AI platform.

Sort its metrics into those that run without ground truth, such as faithfulness and answer relevancy, and those that need an expected output, such as contextual recall, contextual precision, and correctness. Confident AI, the managed platform built around DeepEval, offers role-based access control (RBAC), audit logs, and on-premises deployment on higher-tier plans, so check which plan your requirements land on.

Improve your AI evals with Splunk Agent Observability

When you're responsible for a live agent, ad-hoc spot checks leave you waiting for customers to find the failures your dashboards missed. Gartner forecasts LLM observability investments will rise from 15% of GenAI deployments to 50% by 2028.

Splunk Agent Observability changes that by making evaluation affordable at production scale:

• Evals without ground truth: Splunk Luna evaluators score GenAI outputs without predefined correct answers, comparable to ChainPoll with GPT-4.1 as a judge on context adherence (F1 0.95 versus 0.96), per the Luna-2 research paper.
• Evaluation at production scale: The product page says evaluation and guardrails run on every interaction in production, not a sampled slice.
• Automatic failure detection: Signals analyzes production traces to surface security leaks, policy drift, and cascading failures you didn't know to look for, then generates an LLM-as-a-judge evaluator from any identified signal in one click.
• Agent-native evaluators: Nine agentic evaluators, including Action Completion and Tool Selection Quality, evaluate multi-step agent behavior rather than isolated responses.
• Flexible deployment: Available as SaaS in Splunk Observability Cloud or on-premises, where Splunk Luna evaluators run on your own compute so application data stays inside your environment.
• Broad integrations: Native support for LangChain, LangGraph, CrewAI, and the OpenAI Agents SDK through the splunk-ao Python SDK, plus OpenTelemetry for framework-agnostic tracing.

Splunk Agent Observability is the intelligence layer for trusted agentic operations, turning AI quality assurance from guesswork into an engineering discipline. Splunk has always focused on the visibility and insight that keep digital systems secure and reliable, and that now covers the agents making decisions inside them. Read The Agentic Shift: Redefining Observability for the AI Era to see where agent observability goes next.

FAQs about AI evaluation tools