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When we looked across 12 million production agent logs at Guardy, 78% of the failures had no error code. No timeout, no crash, no tool call that visibly stopped the run. Just wrong answers, abandoned goals, and hallucinations that passed format validation and kept going. That's the actual problem these tools are trying to solve, and it's why the choice between Braintrust and Guardy isn't really a head-to-head comparison. They're built for different moments in an agent's lifecycle.
Braintrust is an eval-first platform designed for teams iterating on prompts and models before they ship. We built Guardy for what happens after you ship, when real users expose behavior that no pre-release eval anticipated. According to a Cleanlab 2025 production survey cited on our blog, only 5% of AI agents that reach production have mature monitoring. That gap is exactly where silent failures compound.
The Side-by-Side, Before We Get Into the Details
The table below maps the dimensions that matter most when choosing between these tools. Braintrust information is drawn from their public documentation and third-party coverage. Guardy information reflects our own platform.
| Dimension | Braintrust | Guardy |
|---|---|---|
| Primary use case | Pre-release eval iteration, prompt/model experimentation | Production monitoring, silent-failure detection, live agent debugging |
| Tracing and replay | Traces logged for eval review; replay within eval runs | Session-level tracing (inputs, outputs, latency, token cost at every step); replay and fork from any intermediate step |
| Evaluation approach | LLM-as-a-judge scorers; offline evals against labeled datasets; online scoring on production traces | Post-trained classifiers fine-tuned on each customer's own agent traffic; built-in classifiers for hallucinations, bad tool calls, agent forgetfulness, jailbreaking; custom classifiers from 3-4 example logs in under a minute |
| Silent-failure detection | Evals must be defined in advance; production drift outside defined criteria is not flagged | Automated detection of behavioral anomalies including goal abandonment, user frustration, and hallucinations in live traffic |
| Full-log vs. sampled classification | Sampling-based online scoring | Classifies every interaction, not a sample |
| Custom classifiers | Custom scoring functions in eval use | Custom classifier instantiation from 3-4 example logs, deployed in under a minute |
| Real-time alerting | Not the primary design; CI/CD gating on pull requests | Real-time Slack alerts on error spikes and behavioral anomalies with source-code-level failure pinpointing and fix suggestions |
| Prompt A/B testing | Side-by-side comparison against a fixed offline dataset | Production A/B testing against real traffic with statistical significance gates |
| CI/CD integration | GitHub Actions integration; blocks merges when regression scores drop below threshold | Instrumentation via OpenTelemetry, LangChain, LangGraph, or custom Python agents |
| Pricing model | Tiered (usage and seats) | Usage-based |
| OpenTelemetry support | Not the primary integration path | Native |
Braintrust Is Good at What It's Actually Built For
Braintrust is purpose-built for structured eval workflows before a release ships. Define scoring criteria, assemble a labeled dataset, run evals in a CI/CD pipeline, block merges when regression scores fall below threshold. That's the loop, and it's well-designed for that specific job.
Braintrust's documentation describes this directly: their integrated approach turns agent evaluation from a collection of scripts into an enforceable release process. For teams doing active prompt or model iteration, that discipline matters. CI/CD gating on every pull request is useful if you're iterating fast and need to know when you've broken something you'd already fixed.
The experiment tracking, dataset versioning, and side-by-side prompt comparison give engineering and product teams a shared workspace for evaluating behavioral changes. Third-party coverage of Braintrust's pricing and feature set notes that the eval infrastructure is solid, the tracing is detailed, and having experiments, datasets, and prompt management in one place saves time.
Braintrust also runs online scoring on production traces automatically as they're logged, using LLM-as-a-judge scorers to assess quality when no ground truth exists for live requests. That closes part of the production visibility gap and it's worth acknowledging.
The constraint worth understanding: Braintrust is strongest when you already know what you're testing for. Its eval use is organized around pre-defined criteria. That works well for regression testing against known failure modes. It works less well for the failure modes you haven't defined yet, the ones that only show up at scale in production. That's not a criticism, it's just the architectural reality of a pre-release tool.
For a deeper look at where Braintrust fits in the broader LLMOps landscape, our Arize vs Braintrust comparison covers that territory in detail.
What We Built Guardy to Do
Guardy covers the full observability loop: what the agent did (tracing), whether it did it correctly (evaluations), and what to fix when it didn't (alerts and code-level debugging). Most tools cover one of these. We cover all three in one platform.
On tracing, we capture session-level traces at every step, including inputs, outputs, latency, and token costs, across the full agent run. We support instrumentation via OpenTelemetry, LangChain, LangGraph, and custom Python agents, and most teams are instrumented within minutes. If you want to understand exactly what that tracing layer looks like, our AI agent tracing explainer walks through the full model.
On evaluations, we classify every interaction in production, not a sample. We use post-trained models fine-tuned on each customer's own agent traffic. We ship built-in classifiers for hallucinations, bad tool calls, agent forgetfulness, and jailbreaking. If a team needs to detect a failure mode that doesn't fit a built-in classifier, they can instantiate a custom one from 3-4 example logs in under a minute. The classifier trains on their actual traffic distribution, not a generic rubric.
When something's wrong, we send real-time Slack alerts with source-code-level failure pinpointing and specific fix suggestions, not just a log entry pointing at the session. Infrabase.ai's coverage of Guardy notes that we diagnose root causes by analyzing conversation patterns, model outputs, and tool interactions, then recommend specific fixes.
We also offer time-travel debugging: replay any agent state at any point in execution history, and fork from any intermediate step to test an alternative prompt or tool path side-by-side. That combination of full-log classification, step-level attribution, and replay/fork is the part of our stack that has no equivalent in a pre-release eval use.
Silent Failures Are the Whole Problem, and Most Teams Are Flying Blind
Silent failures are the defining challenge of production AI agents. As arXiv research on multi-agentic AI trajectories describes: unlike microservice applications where errors are typically explicit with error codes and predictable, failures in agentic systems can often be silent, occurring without generating clear error signals while still deviating from intended behavior.
In concrete agent terms, that looks like this: a customer support agent returns a wrong refund policy without any tool error. A research agent abandons a multi-step goal after losing context three turns back. A hallucinated tool call passes format validation and writes a bad record to a downstream system. VentureBeat's coverage of silent failures in enterprise AI put it plainly: the most expensive AI failure in enterprise deployments didn't produce an error. No alert fired. No dashboard turned red. The system was fully operational. It was just consistently, confidently wrong.
OnPage's 2026 observability tool comparison identifies this as one of the biggest challenges in production AI: a model can hallucinate convincing answers, drift from intended behavior, retrieve irrelevant context, call the wrong tools, or generate outputs that gradually decline in quality without triggering any traditional monitoring alert.
Three things separate Braintrust and Guardy on this specific problem:
Coverage. Braintrust's online scoring operates on a sample of production traces. We classify every interaction. The math matters: as we analyzed in our Arize vs Guardy comparison, if a production agent handles 100,000 interactions per day and monitoring samples 5% of them, a failure mode affecting 0.3% of sessions shows up in roughly 15 sampled logs per day. At that volume, it looks like noise. We classify all 100,000 interactions. That same 0.3% failure rate surfaces as 300 flagged logs, visible, actionable, and not mistakable for noise. OneUptime's 2026 cost analysis echoes this: head-based sampling doesn't work for AI workloads.
Step localization. Knowing a session failed isn't the same as knowing which intermediate step caused it. Our replay and fork capability lets teams work through to the exact tool call or reasoning step where the agent went off-track. Braintrust surfaces failure scores at the eval level. Locating the causal step inside a multi-turn run requires additional investigation.
Time-to-fix. We surface Slack alerts with source-code-level pinpointing and fix suggestions. A team can receive an alert, see the affected code path, and push a fix in the same workflow. Braintrust's strength is converting a known failure mode into a regression test. That's useful, but it assumes you've already identified what went wrong.
Guardy is the clear answer when the question is whether your live agent is failing users right now and how you find out before they do. For everything else on silent failure detection in production, see our LLM observability explainer.
Regression Testing and CI/CD Gating: Braintrust Is Better Here
Braintrust has deeper, more mature tooling for teams whose primary need is systematic pre-release testing. Dataset versioning, scoring function libraries, and CI/CD gating represent real investment in a workflow that a lot of teams genuinely need.
The PyCharm Blog's 2026 piece on LLM evaluation and AI observability draws the distinction cleanly: LLM evaluation determines if an AI agent can work, while AI agent observability determines if it is working. Evaluation tests an agent's basic capabilities before and during deployment. Observability provides real-time visibility into reasoning and operational health once the agent is live. Braintrust owns the first half of that sentence with more purpose-built tooling.
The gap that matters for production teams: eval definitions written before deployment often don't match the failure modes that appear in real user traffic. We reviewed this in our LLM observability platform roundup: production drift is invisible to a static eval use. LangChain's observability guide frames the best-practice loop as: instrument everything, close the loop from production trace to regression dataset, then let automated evaluations replace instinct-based release decisions. That loop needs both tools working together. It's not a choice between them.
The practical implication: teams using Guardy in production and Braintrust in CI/CD can feed Guardy-labeled failure traces back into Braintrust as regression datasets. Production observations become pre-release test cases. The loop closes.
For teams who want to go deeper on building regression suites from production failures, our article on AI agent regression testing covers the methodology in detail.
Prompt A/B Testing Depends Entirely on Where Your Agent Lives
Both tools offer prompt comparison. The difference is where in the agent lifecycle that comparison happens.
Braintrust's side-by-side comparison runs against a fixed dataset offline. Two prompt variants, run both against a labeled eval set, score the results. That's useful for controlled pre-release experiments where you want clean comparisons before real users are involved.
We run A/B testing in production against real traffic with statistical significance gates. The reason this matters is distribution. Offline A/B testing on a curated dataset can produce results that don't replicate in production because real user inputs have different distributions, edge cases, and behavioral patterns than a hand-labeled eval set. Our production testing catches behavioral drift that offline experiments miss.
The split here is clear: Braintrust for controlled pre-release prompt experiments with labeled eval sets, Guardy for production A/B testing with real traffic and statistical rigor. If you want the full methodology for running statistically valid prompt experiments in production, our prompt A/B testing guide covers it step by step.
How to Actually Decide
The right choice depends on whether your agent is live and serving real users. That single variable drives almost everything else.
Teams in active pre-release iteration. If your agent isn't in production yet and your primary need is comparing prompts, selecting models, and building a regression use before release, Braintrust fits that workflow well. We'd recommend it as your primary tool here.
Teams with production agents experiencing silent failures. If your agent is live and you're discovering problems through user complaints, support tickets, or periodic manual reviews rather than automated detection, we'd recommend Guardy. Instrument once via OpenTelemetry, LangChain, LangGraph, or a custom Python agent, and you get full-log classification, real-time Slack alerts, and step-level debugging across every session, not just the ones that crash.
Teams who want both. This is the architecture we see most often with mature agent teams: Braintrust in the CI/CD pipeline for pre-release eval gating, Guardy in production for live monitoring and debugging. Instrument with OpenTelemetry once, stream to Guardy for production classification and alerts, then use Guardy-labeled failure traces to build new regression datasets in Braintrust. Production observations feed pre-release tests. The reliability loop closes.
Teams where Guardy isn't the right starting point. If you have zero live production traffic and purely need an eval use for model selection during development, Braintrust or a purpose-built eval tool is the better starting point. We're designed for agents with live traffic. Without it, our production classification layer has nothing to classify.
Gartner predicts that more than 40% of agentic AI projects will be canceled by 2027 due to escalating costs and unclear business value, a problem that comes largely from lack of visibility into how AI systems behave once deployed. Having pre-release evals is necessary. It's not sufficient. The Sentry developer guide on agent observability frames it well: effective agent monitoring requires aggregate dashboards and detailed failure traces. Most platforms provide only one.
For a broader look at how production monitoring fits into the full AI agent reliability loop, see our LLM monitoring explainer and our agentic AI observability best practices guide.
FAQ
Which is better for detecting silent failures in production AI agents, Braintrust or Guardy?
Guardy is better suited for silent failure detection in production. Braintrust's eval use requires failure modes to be defined in advance and evaluates against pre-labeled datasets. Silent failures, wrong answers, goal abandonment, hallucinations that pass format validation, only appear in live traffic at scale. We classify every production interaction using post-trained classifiers fine-tuned on your agent's own traffic, so failure patterns surface even when you didn't know to look for them. Across 12 million logs analyzed at Guardy, 78% of issues were silent behavioral failures, not explicit crashes. Braintrust isn't designed to catch those.
Which tool is better for turning failures into regression tests and release gating?
Braintrust. Its CI/CD integration via GitHub Actions runs eval suites on every pull request and blocks merges when regression scores fall below defined thresholds. Dataset versioning and scoring function libraries make it straightforward to convert a known failure mode into a repeatable test. That said, the regression tests are only as good as the failure modes you've already identified. We complement this by surfacing new failure modes from production logs that can then be added to Braintrust's regression suite, closing the loop from live behavior back to pre-release gating.
Which platform offers better full-log classification at scale?
Guardy. We classify every interaction in production. Braintrust's online scoring operates on a sample. The difference isn't academic: as we analyzed in our Arize vs Guardy comparison, a failure mode affecting 0.3% of sessions at 100,000 daily interactions shows up as roughly 15 sampled logs per day, indistinguishable from noise. We surface the same failure rate as 300 flagged logs. Low-frequency failures are exactly the kind that compound quietly for weeks before they become visible through user complaints.
Do Braintrust and Guardy replace APM tools like Datadog?
Neither tool is a replacement for general-purpose APM. Traditional APM catches infrastructure-level failures: crashes, timeouts, error codes, latency spikes. Both Braintrust and Guardy operate at the behavioral layer, evaluating what the agent said and did, not just whether the service stayed up. We complement APM rather than replace it. If you're evaluating Datadog's fit for AI agent monitoring specifically, our Datadog alternatives guide covers where traditional APM falls short for LLM workloads.
Which one is better for prompt and model iteration during development?
Braintrust is better suited for prompt and model iteration workflows during development. Its side-by-side prompt comparison, experiment tracking, and dataset management give teams a structured environment for evaluating behavioral changes before anything reaches users. Guardy's prompt A/B testing is designed for production traffic, where statistical rigor against real user inputs matters most. If you're pre-production and evaluating prompt variants against a labeled dataset, Braintrust is the right tool. If your agent is live and you want to validate a prompt change against real traffic before fully rolling it out, we handle that stage. See our prompt A/B testing guide for the production methodology.
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