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Arize Phoenix and Braintrust are the two tools that come up most when engineering teams start asking how to monitor production AI agents. Both are useful. Neither catches the majority of what goes wrong. Here's where each one excels, where it stops, and what a third approach exists for teams whose agents are already in production and generating failures nobody has thought to search for yet.
The Quick Verdict
Arize Phoenix is the stronger pick when you need deep production tracing and span-level visibility into what your agent actually did, step by step, across a multi-tool or multi-agent run. Braintrust wins when you have an active eval suite and want a tight trace-to-test CI/CD loop that blocks regressions before they ship.
The real gap neither fills: classifying every production interaction at scale to surface the failures nobody thought to define as a test case yet. From our analysis across 12 million logs, around 78% of agent failures are not clean errors or timeouts. They're silent regressions where the user gets a wrong or useless answer and leaves. Hallucinations were the top failure category, followed by user frustration, then agent forgetfulness or laziness. Traditional tracing misses all three. Eval pipelines only catch the ones you already knew to look for.
Vellum's practical guide to AI observability captures the pattern well: AI agents fail silently with clean responses that are confidently wrong, and logs and metrics alone can't answer for that. Fiddler's research on AI agent failure rates reinforces the same point.
Quick Comparison: Arize Phoenix vs Braintrust
| Dimension | Arize Phoenix | Braintrust | What's Still Missing |
|---|---|---|---|
| Primary use case | Production tracing and span-level forensics | Trace-to-test eval loop and CI/CD regression gating | Full-coverage behavioral classification for unknown failures |
| Instrumentation model | OpenTelemetry / OpenInference SDK | AI Proxy and SDK; npx braintrust push | Both require you to define what to look for before you can catch it |
| Trace and session replay | Deep span-level replay; strong multi-agent visibility | Traces feed eval datasets; not optimized for post-hoc forensics | Neither supports forking from an arbitrary intermediate step and rerunning |
| Evaluation workflow | Evaluation hooks present; workflow is closer to export-and-evaluate than native closed loop | Native LLM-as-judge scoring; tightest eval pipeline in this comparison | LLM-as-judge scores only what you ask it to score |
| CI/CD regression gating | Limited native gating; tracing-first tool | Strongest in this comparison; blocks deploys on regression | Only blocks regressions on failures you've already defined |
| Sampling and coverage | Sampling-based; not designed to classify every log | Promotion-based; you choose which traces become test cases | Low-frequency silent failures fall below the sampling threshold |
| Self-hosting and open source | Phoenix is open source and self-hostable; Phoenix Cloud is managed | SaaS; no self-hosted option | , |
| Silent failure detection | Not a design goal; relies on metrics you define | Not a design goal; relies on evals you define | Requires post-trained classification models running on full log volume |
Arize Is the Right Call When You Need Forensics
Arize Phoenix is built around observability-first principles. Its core value is span-level visibility into what your agent actually executed, step by step, using OpenTelemetry and OpenInference semantic conventions. Arthur AI's guide to agent observability identifies OTel and OpenInference as the industry standard for vendor-neutral agent tracing, and Arize is one of the strongest implementations of that standard.
Where Arize genuinely excels: multi-agent handoffs, retrieval chain inspection, and tool call visibility in complex pipelines. If your agent runs for multiple steps across multiple tools and something went wrong somewhere in the middle, Arize gives you the best chance of finding which span it was. Phoenix Cloud is the managed offering; Phoenix itself is open source and self-hostable, which matters for teams with data residency requirements or a preference for infrastructure control. Arize's own roundup of observability tools notes that Phoenix retains traces in context graphs within its native database, enabling queryable production tracing and span-level forensics.
Where it's weaker: evaluation workflows are more manual and export-dependent than Braintrust's. Getting from a bad trace to a formalized test case requires pipeline work that Arize doesn't do natively. And for high-volume production agents where failures are non-deterministic, a trace-and-end-state-only approach stops being sufficient. When tool calls and branching behavior vary across runs, the spans you need to inspect aren't always the ones you thought to instrument. Confident AI's comparison of observability tools makes this point directly: traditional observability-first tools like Arize excel at tracing but don't automatically turn traces into evaluation workflows or prevent unknown failures.
Braintrust Is the Right Call When You Already Know What You're Testing For
Braintrust is positioned as a development-loop accelerator as much as a monitoring tool. Its core workflow is: capture a production trace, promote it to an eval dataset, run LLM-as-judge scoring, gate deploys in CI/CD. That loop is the tightest in this comparison and it's a real differentiator for teams who have active regression prevention needs.
Where Braintrust genuinely excels: instrumentation friction is low, with an AI proxy model and an npx braintrust push workflow that gets teams logging quickly. Braintrust's own CI/CD eval guide describes the primary strength accurately: production traces promote to eval datasets, LLM-as-judge scoring runs against them, and failing evals block regressions at the deploy gate. For teams who know what they're testing for, that's a well-designed system.
Where it's weaker: production observability is shallower than Arize. Braintrust is better at evaluating the interactions you promote to test suites than at surfacing the ones you didn't know to look for. LLM-as-judge evaluators are generic by default. They score what you ask them to score. A subtly wrong answer, a frustrated user, an agent that silently forgot context from turn three: none of those surface unless you've already defined them as eval criteria. From our own analysis, evals worked well when agents were simpler chatbots but they fall short as production issues increasingly come from behaviors you couldn't predict beforehand. There's also a scale problem: with agents that run hundreds of tool calls over sessions lasting hours, generic LLM-as-judge scoring loses accuracy compared to models trained on the specific patterns of your agent's traffic.
Research on LLM-as-judge evaluators confirms the structural limitation: these evaluators have known biases and can be gamed by adversarial outputs, which limits their ability to catch failures outside their training distribution.
Both Tools Stop Short of Step-Level Replay
On raw tracing depth, Arize wins. Span-level visibility into tool calls, retrieval chains, and multi-agent handoffs is its primary design goal. If you need to understand what happened during a specific agent run, Arize gives you the most granular post-hoc forensics of the two.
Braintrust's tracing is sufficient for its eval loop but isn't optimized for forensic investigation. It's better understood as trace capture for the purpose of test case promotion than as a debugging tool in its own right.
Both tools share a gap that doesn't come up often in comparisons: neither supports forking from an arbitrary intermediate step in an agent run and rerunning from that point with controlled changes. The practical debugging workflow for a silent failure looks like this: detect the bad interaction, classify why it failed, replay to the exact step where the failure originated, fork from that step, and rerun with a different prompt or tool configuration. That loop doesn't exist natively in either platform.
One Series B finance company we worked with launched an agent for accounts receivable. The agent was taking in vendor PDFs, extracting data, and computing quotes. It looked fine: quotes varied across PDFs and prices seemed approximately right. But it wasn't actually ingesting the initial PDF. It was hallucinating quotes based on other context in the session. The run succeeded end-to-end from a trace perspective. The span-level data showed tool calls completing. Nobody knew until we ran full-coverage classification and found the extraction step was failing silently on every run. A trace-and-end-state-only approach, which is what most OTel-based tools produce, wouldn't have caught it. LangChain's piece on LLM observability makes the same point: APM dashboards can show healthy latency and error rates while agents are confidently providing incorrect information.
Winner for raw tracing depth: Arize Phoenix. Winner for structured promotion to test suites: Braintrust. Neither for replay-and-fork at the step level.
Braintrust Has the Tightest CI/CD Loop, But Only for Failures You've Already Defined
This is Braintrust's strongest territory. The concrete workflow: a production trace surfaces in the dashboard, you promote it to a dataset, LLM-as-judge evaluators score it against your defined criteria, and a failing score blocks the next deploy in CI/CD. Braintrust's own guide on LLM-as-judge vs human-in-the-loop evals is honest about the boundary condition here: these evaluators score what teams ask them to score, which means failures you haven't defined as test criteria stay invisible.
Arize has evaluation hooks and LLM-as-judge support, but the workflow is closer to export-and-evaluate than a native closed loop. It's an observability tool that supports evaluation rather than an evaluation tool with observability attached. JetBrains' guide on LLM evaluation and agent monitoring captures the shared limitation: online evaluation reveals edge cases in production that offline test suites never anticipated, but both approaches require pre-defined test criteria to score against.
The problem both share: an eval pipeline only protects you against failures you've already thought to define. As production agents handle more complex, multi-turn workflows, the failure modes that hurt users are often the ones that fall outside any eval suite. You can't write a test case for a failure mode you haven't discovered yet. That's why we use post-trained classification models that run on the full log volume. The point isn't to replace evals but to surface the failure patterns that should become your next eval criteria, before a user files a complaint.
Winner for CI/CD regression gating: Braintrust. Winner for observability-native eval hooks without a separate test pipeline: Arize. Neither for discovering failure modes you haven't yet defined.
Sampling Is Why You're Missing the Failures That Matter Most
This is the dimension neither Arize nor Braintrust addresses prominently, and it's the one that matters most at scale.
Both tools operate on a model where you either sample production traffic or selectively promote interactions to eval datasets. Neither is designed to classify every log against a behavioral model trained on your agent's specific traffic patterns. For teams running millions of interactions per month, that creates a concrete risk: if your agent hallucinates 0.5% of the time on a specific input pattern, and your monitoring samples 10% of production traffic, the expected number of captured instances is 0.05% of your total volume. You'll never see a statistically significant cluster. You won't know the failure exists until a user complains or churns silently.
Grafana's research on tail sampling documents the problem: uniform head sampling at 10% misses rare failures affecting small slices of traffic, and low-frequency edge cases stay invisible unless sampling rate is significantly increased. Distributed tracing research puts a number on it: sampling creates a query miss rate of up to 27%, forcing teams to analyze only the traces they happened to capture. Diagnostic approaches that compare normal and abnormal traces fail when the abnormal traces were never sampled.
A practical way to audit your current tool's coverage: generate synthetic edge cases that represent your known risk scenarios, inject them into a test traffic stream at low frequency, and verify they surface in your monitoring dashboard within a statistically reasonable window. If they don't, your sampling rate is too low to catch the failure modes that matter most.
We've processed over 12 million logs using post-trained classification models rather than LLM-as-judge calls or sampling-based approaches. The cost difference is significant: passing every log through a frontier LLM for classification would require a budget in the range of $100,000 or more for that volume. Post-trained models purpose-built for classification run at a fraction of that cost and with meaningfully higher accuracy on your agent's specific patterns, because they're trained on your data, not a generic distribution. The goal is full log coverage without the cost structure that makes full coverage feel impractical.
Which Should You Choose?
Choose Arize Phoenix if: Your primary need is deep production forensics. You're running multi-agent or tool-heavy architectures and need span-level visibility into what each step did. You want OTel-compatible instrumentation that works with your existing observability stack. You prefer open-source and self-hosted deployment options. You're comfortable building your own eval pipeline on top of the trace data Arize provides.
Choose Braintrust if: You have an active eval suite and a CI/CD pipeline where regression prevention is the primary goal. Your team already knows the failure modes you're testing for and you want a tight trace-to-test workflow that gates deploys automatically. You're comfortable with SaaS-only deployment and LLM-as-judge scoring as your primary evaluation mechanism.
Consider adding a classification layer if: Your agents are in production and handling high interaction volume. You're seeing user complaints or churn that your current monitoring didn't flag first. You want to know which failure modes exist before you write eval criteria for them, not after. You need to catch patterns like hallucinations, user frustration, and agent forgetfulness at scale without manually reviewing logs or promoting samples.
Both Arize and Braintrust assume the failure you care about is one you're already hunting. They're excellent tools for that job. The problem is that 84% of CIOs report lacking formal processes for tracking AI accuracy, and silent failures in agentic systems don't trigger alerts until damage is already done. The classification layer that covers what neither tool handles isn't a replacement for tracing or evals. It's the layer that tells you what to trace and what to eval next.
We saw this concretely with a Fortune 1000 customer running production agents across supply chain, HR, and marketing workflows. Their error rate dropped from 20% to under 10% in a single week once full-coverage classification identified the failure clusters their existing monitoring hadn't surfaced. The tracing was already in place. What was missing was the behavioral classification layer on top of it.
With Guardy, teams can instantiate custom classifiers for whatever failure mode matters to their specific agent, not just the built-in categories. Check three or four example logs, deploy a fine-tuned classifier in under a minute, and start catching that pattern across every subsequent interaction. Built-in classifiers cover hallucinations, bad tool calls, agent forgetfulness, and jailbreaking. Custom classifiers cover whatever your agent does that those categories don't.
Other Alternatives Worth Considering
Galileo is worth evaluating for teams focused on hallucination detection and failure pattern recognition. Galileo's own framing positions it around automated anomaly detection across large interaction volumes, which is a closer fit to the classification posture than either Arize or Braintrust.
LangSmith is the natural choice for teams already deep in the LangChain ecosystem who want tight trace-to-eval integration without switching stacks. It's strongest as a development-time tool. Where teams tend to outgrow it is on the semantic analysis side: it shows you logs, primarily end-to-end, without giving you much to work with on top of that. It's adequate for sampling and checking overall quality but lacks the customization that production teams need as their agents grow more complex.
Guardy belongs in the stack when your agents are in production and you need silent failure classification at scale, not just tracing or eval workflow tooling. We integrate in minutes via OpenTelemetry, LangChain, LangGraph, or custom Python agents and run classification against every interaction, not a sample. For a broader look at how these tools compare across more dimensions, our article Best LLM Observability Platforms in 2026 covers eight platforms ranked by debugging depth.
If you're specifically evaluating how Guardy compares directly to one of these tools, see our dedicated comparisons: Arize vs Guardy and Braintrust vs Guardy. For a deeper look at what evals can and cannot catch on their own, Your Agent Passing Evals Means Nothing covers the structural limits of eval-only approaches in production.
FAQ
Which is better, Arize Phoenix or Braintrust?
It depends on what problem you're solving. Arize Phoenix is better for deep production tracing and span-level forensics on complex agent runs. Braintrust is better for teams with active eval suites who need a tight trace-to-test CI/CD loop. Neither is designed to classify every production interaction to catch silent failures like hallucinations, user frustration, or agent forgetfulness at scale. If that's the gap you're trying to close, neither tool alone is sufficient.
What are the key differences between Arize and Braintrust for agent monitoring?
Arize is observability-first: it gives you span-level visibility into what your agent did using OpenTelemetry and OpenInference instrumentation. It's stronger for forensic investigation of specific bad runs. Braintrust is eval-first: it's designed to turn production traces into test cases, run LLM-as-judge scoring, and gate CI/CD deploys on regression. It's stronger for teams who know what failure modes to test for. The key shared gap: both tools assume you've already defined the failure you're looking for. Neither surfaces failures from outside your defined eval criteria or metric set.
Which is better for evaluation workflows and CI/CD gates?
Braintrust is the clear winner here. Its trace-to-test promotion workflow, LLM-as-judge scoring, and CI/CD quality gates are the strongest in this comparison. Arize has evaluation hooks but the workflow is closer to export-and-evaluate than a native closed loop. If blocking regressions before deploy is your primary goal and you already have an eval suite, Braintrust is the better fit.
Does Arize Phoenix support deep agent tracing and session replay?
Yes, this is Arize's primary design goal. It provides span-level visibility into tool calls, retrieval chains, and multi-agent handoffs using OTel and OpenInference conventions. Where it stops short is on replay-and-fork mechanics: Arize shows you what happened but doesn't natively support forking from an arbitrary intermediate step and rerunning with controlled changes. That step-level debugging loop requires additional tooling.
Does Braintrust turn production traces into test cases and enable regression prevention?
Yes, that's Braintrust's core workflow. Production traces surface in the dashboard, you promote them to eval datasets, LLM-as-judge evaluators score them, and failing evals block deploys in CI/CD. The important boundary condition: this only protects against regressions on failure modes you've already defined as eval criteria. Failures your team hasn't yet thought to test for, including novel hallucination patterns or emerging user frustration signals, won't surface through this pipeline until someone first notices them in production.
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