The 5 Best AI Agent Evaluation Tools for Product Teams

AI agent evaluation tools, a specialized branch of LLM evaluation tools, measure whether an agent completes its tasks correctly, safely, and reliably. They capture a trace of each run, score its steps and final output with code checks, LLM judges, or human review, and flag regressions before and after release. The strongest also show how agent quality changes what users do next.

Most stacks combine three layers. Offline evals test the agent against fixed datasets before you ship. Tracing and online evals score live runs in production. Outcome analytics ties those runs to user behavior.

The KDD 2025 survey of LLM agent evaluation by Mahmoud Mohammadi and colleagues at SAP Labs notes that "offline evaluations typically lack the nuance to fully address the wide range of responses an LLM agent may be able or expected to provide."

We assessed every tool against six criteria. They cover what engineers need when testing an agent before release, and what product managers need once they own its results in production. These criteria also reflect gaps the KDD 2025 survey says current research often overlooks, including reliability guarantees, long-horizon interactions, and compliance.

Trace depth. Can you see every step of a run, including planning, tool calls, retrieved context, and the final action? Agents fail mid-chain, so a tool that only stores the final output hides where things broke.

Eval methods. Look for a mix of code-based checks, LLM-as-a-judge scoring, and human review. Automated judges scale, but they carry known flaws. A survey of LLM-as-judge methods by researchers at Tsinghua University and Renmin University of China found that "the evaluation results of LLMs are often influenced by the prompt template, which can lead to biased or inconsistent assessments."

CI/CD regression testing. Can you run evals automatically on every prompt, model, or code change and block a release that gets worse?

Production monitoring. Agent quality drifts as models update and users ask new things. The tool needs to score live traffic and surface clusters of failures, not just single bad runs.

Pricing and openness. Is there an open-source option or free tier? Can you self-host? How does cost scale with trace volume?

Connection to user behavior and business outcomes. Can you see whether better agent runs lead to higher retention, conversion, or revenue without exporting data to a warehouse? The same survey argues that "standard, short-term evaluations cannot capture phenomena such as performance drift, context retention, or the cumulative effect of decisions on business outcomes."

How we selected and evaluated the tools

We started with the platforms search results and AI Overviews surface most often for this category (Arize Phoenix, LangSmith, DeepEval, and Braintrust), plus Amplitude as the analytics-led option. We left out Promptfoo, which focuses on prompt testing and red-teaming rather than agent tracing. We reviewed each remaining tool's public documentation and pricing, as of October 2026, against the six criteria above, in the same order, and noted one limitation per tool. Because this guide is written for product teams, we weighted the outcome criterion most heavily, which is why Amplitude ranks first even though the eval-first tools are stronger on pre-release testing.

1. Amplitude Agent Analytics

Amplitude Agent Analytics is the evaluation layer for teams that own what the agent does for users. It ties agent traces and eval scores to conversion, retention, and revenue, so conversation quality sits next to the funnels and cohorts in product analytics.

• Trace depth: every user message, tool call, agent response, and session end becomes an Amplitude event, and OpenTelemetry GenAI traces can be routed in without re-instrumenting.
• Eval methods: every closed session is scored on seven built-in signals with no setup, including Task Completed, Response Quality, and User Friction, plus custom LLM-as-a-judge and code evaluators and human review.
• CI/CD: scores production sessions. Pair it with an offline eval tool for pre-release tests.
• Production monitoring: Topics clusters online sessions so you can see which ones have low task success or high friction, instead of one-off errors.
• Pricing and openness: included on every plan, including the Free plan with 5,000 monthly agent sessions. It isn't open source.
• Outcomes: its strongest criterion. Quality scores sit beside conversion, retention, and revenue rather than in a separate tool.

The trade-off is timing. Agent Analytics scores live sessions, so it surfaces problems once real users hit them. For a new agent with little traffic, an offline eval suite will give you signal sooner.

G2 rates the Amplitude platform 4.5 out of 5 across 2,997 reviews. A software engineer at a mid-market company says, "The thing I appreciate most is how quickly I can get from a question to an actual answer." A product manager adds that "session replay has been genuinely useful for spotting exactly where users drop off in a flow."

Pricing: Usage-based. The Free plan includes 2 million events and 5,000 agent sessions a month. Plus starts at $0 and scales with your event volume, while Growth and Enterprise have custom pricing, according to Amplitude's pricing page (as of October 2026).

Best for: Product and growth teams that want to know whether agent quality keeps users.

2. Braintrust

Braintrust is built around evals. Teams use it to score prompts, models, and agent versions against datasets, then compare results side by side before shipping.

• Trace depth: captures span-level traces, including tool calls.
• Eval methods: automated scoring against datasets, including LLM-as-a-judge.
• CI/CD: its experiment workflow suits regression testing before each release.
• Production monitoring: logs production traffic for online scoring, and its Topics feature clusters logs daily to surface recurring failures and edge cases.
• Pricing and openness: commercial rather than open source. It runs as SaaS, with a self-hosted data plane on Enterprise, and cost scales with data processed and scores.
• Outcomes: it shows which version scored higher, not which version kept more users. Tying scores to retention or revenue means joining the data in another tool.

The gap is control over where data lives: self-hosting the data plane is reserved for Enterprise, so smaller teams with residency rules may need a different option.

G2 rates Braintrust 4.3 out of 5 across 58 reviews. A developer says, "I like how Braintrust makes it easier to evaluate and monitor AI applications in one place."

Pricing: Free Starter plan with 1 GB of processed data, 10,000 scores, 14-day retention, and unlimited users; Pro costs $249 a month, according to Braintrust pricing (as of October 2026).

Best for: Engineering teams running structured, dataset-driven evals before each release.

3. Arize (Phoenix and AX)

Arize ships two products. Phoenix is source-available under the Elastic License 2.0 and free to self-host, and AX is the commercial platform for teams that outgrow it.

• Trace depth: Phoenix gives you trace capture you can run yourself, one of the easiest ways to start without a contract.
• Eval methods: includes LLM-as-a-judge scoring.
• CI/CD: eval runs can be scripted into pipelines.
• Production monitoring: AX adds managed, production-scale monitoring, and its Signal feature groups recurring failure patterns into issues on a schedule. Phoenix doesn't document an equivalent.
• Pricing and openness: Phoenix runs on your own infrastructure. AX is billed on spans and data volume.
• Outcomes: quality data stays separate from product behavior, so retention and revenue questions need a separate analytics tool.

Watch the split between products: before you commit, check which features sit in Phoenix and which require AX.

G2 rates Arize AX 4.4 out of 5 across 76 reviews. A technical project manager says, "Arize AI has made it much easier to monitor model performance in production and catch issues like data or concept drift before they significantly impact output quality."

Pricing: Phoenix is free to self-host. Arize AX Free includes 25,000 spans a month with 15-day retention, and AX Pro starts at $50 a month, according to Arize pricing (as of October 2026).

Best for: Teams that want to start with self-hosted tracing and keep a commercial upgrade path.

4. LangSmith

LangSmith is LangChain's platform for tracing and evaluating LLM apps and agents. It's the default choice for teams already building on LangChain.

• Trace depth: its core strength, with step-by-step runs that show tool calls and intermediate outputs.
• Eval methods: dataset-based testing and LLM-as-a-judge scoring.
• CI/CD: supports running evals as part of a release process.
• Production monitoring: monitors live traces, and its Insights reports group traces into categories that reveal failure modes, on the Plus and Enterprise plans.
• Pricing and openness: a commercial product priced per seat. Self-hosting is an add-on to the Enterprise plan.
• Outcomes: business outcomes sit outside its scope, so retention and conversion analysis means moving data to another system.

Outside LangChain, the fit is less natural. Teams on other frameworks may find less of a head start than LangChain users get.

G2 rates LangSmith 4.4 out of 5 across 78 reviews. A senior software engineer says, "What I like most about LangSmith is how much easier it makes debugging AI applications."

Pricing: Free Developer plan with 1 seat and 5,000 base traces a month; Plus costs $39 per seat a month, according to LangChain pricing (as of October 2026).

Best for: Engineering teams building agents with LangChain who want tracing and evals in one place.

5. DeepEval (Confident AI)

DeepEval is an open-source evaluation framework from Confident AI, released under the Apache 2.0 license, and Confident AI sells a hosted platform on top. Its documentation separates end-to-end, trajectory-based, and component-level agent evals, a useful way to think about where an agent breaks.

• Trace depth: comes mainly through the Confident AI platform.
• Eval methods: ships with ready-made metrics, many of them LLM-judged.
• CI/CD: code-first. Engineers write evals as tests, which makes it a strong fit for regression checks in the pipeline.
• Production monitoring: handled by Confident AI, where classifiers label traces and Production Signals surface patterns and spikes. Clustering helps propose labels, but you configure the classifiers yourself.
• Pricing and openness: the framework is free to run. The platform is SaaS, with self-hosting on Enterprise, and is billed by tier plus stored trace data.
• Outcomes: it doesn't link scores to user behavior.

It's built for people who write Python. Product managers won't use it day to day, and its heavy reliance on LLM judges carries the bias risks noted above.

G2 rates Confident AI 4.6 out of 5 across 5 reviews, a small sample. A back-end developer says, "I really like the great open source CLI tool that Confident AI provides to run and develop my evals."

Pricing: DeepEval is free. The Confident AI platform's free plan includes 2 seats and 1 project, and Starter costs $200 a month, according to Confident AI pricing (as of October 2026).

Best for: Engineers who want agent evals to run like unit tests in the pipeline.

Open-source and self-hostable tools are a quick way to start. You can run them on your own infrastructure, keep sensitive traces in-house, and avoid a contract while you figure out what you're measuring. The trade-off is upkeep. Someone on your team owns hosting, upgrades, and scaling as trace volume grows.

Commercial platforms take that work off your plate and add collaboration, dashboards, and managed monitoring.

Start with when you need the answer. Agent evaluation splits into two jobs owned by two different teams, and most companies with an agent in production need both.

Before release: engineers testing the agent

ML and platform engineers need to know whether a change is safe to ship. That work runs on offline evals: fixed datasets, regression tests on every prompt or model change, and step-level traces that show where a run broke. Most teams building agents already have this layer in place, and it belongs in the engineering workflow.

If that's your job, weigh trace depth, eval methods, and CI/CD hooks above everything else, and pick the setup that fits your framework and how much of it you want to host yourself.

In production: product teams measuring impact

Once real users reach the agent, the question changes. An offline suite tells you the agent can answer correctly. It can't tell you whether the people using it finished their task, got frustrated, or came back the next week, because that only shows up in live traffic.

Product managers and growth leads need three things here: a quality score on every real session, failures grouped into patterns so they know what to fix first, and those scores next to the conversion, retention, and revenue numbers they're measured on.

That's the job Amplitude Agent Analytics is built for. It scores every closed session automatically, groups sessions into topics so low task success stands out, and keeps the results in the same place as your funnels and cohorts. It doesn't replace pre-release testing. It picks up where those evals stop, and it accepts the OpenTelemetry GenAI traces you already emit, so there's nothing to re-instrument.

A quick way to decide: if your main question is "is this change safe to ship?", invest in pre-release evals. If it's "is the agent working for our users and the business?", you need production analytics. Every team with an agent live has the second question.

An eval tool answers "was the response right?" Behavioral data answers "did the user stay?" You need both, because an agent can score well on every rubric and still frustrate people with slow answers, partial answers, or answers to the wrong question.

Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, citing "escalating costs, unclear business value or inadequate risk controls." Unclear business value is partly a measurement problem, and eval scores alone can't show it.

CashBook, a Y Combinator-backed finance app for small businesses in India, shows why. After wiring its agent to Agent Analytics and grading every session, the team found a 16% error rate in one internal tool. Eighty-six percent of sessions passed the response-quality check, yet by week four, users whose sessions failed retained at 63.9%, against 79.2% for everyone else.

Amplitude's platform covers the behavioral side. Agent Analytics ties quality scores to retention and revenue, and from any session, View Replay opens Session Replay at the moment the conversation started, so you can watch what the user did in the product. When something drops, Amplitude AI Agents run automated diagnostics on what changed and why. Amplitude's own engineering team has also written about task-based evaluation of the analytics agents it ships.

The teams that ship agents people trust run a loop, not a one-time test: offline evals, production traces, then outcome metrics.

The last step is to test changes against those outcomes. With Feature Experimentation, you can roll out a new prompt, model, or tool configuration to part of your traffic and compare retention and conversion, not just eval scores.