A detailed, practical reference comparing evaluation philosophies, data needs, integrations, outputs, and recommended workflows.
Useful for product teams, engineering leads, reliability engineers, and investors evaluating LLM & agent observability solutions.
Core Philosophy Difference
Trace-level evaluation
“Did the agent follow the process correctly?”
ML monitoring & drift
“Was the output correct vs ground truth?”
Human annotation
“Does the answer match expected output?”
112
Noveum.ai
15
Dimension
0
Ground truth required
Executive Summary
Each platform excels in different areas. Understanding these differences will help you choose the right solution for your specific needs.
Full-stack LLM & agent evaluation + compliance platform built around trace-level understanding.
Primary Focus
Evaluates how and why an agent behaved the way it did
Best suited for traditional Machine Learning and statistical monitoring.
Primary Focus
Measures final answer correctness against ground truth
Best suited for human annotation, gold-label creation, and training-data generation.
Primary Focus
Measures final answer correctness against ground truth
Core Difference
“Did the agent follow the process and use the system/tools/context correctly?”
“Was the final answer correct compared to a ground truth?”
This fundamental difference shapes everything else: data requirements, evaluation methods, integration complexity, and ideal use cases.
Feature Comparison
Noveum currently provides 112 calibrated scorers across 15 categories, including Agent, Conversational, RAG, Safety, Hallucination, Quality, and more.
| Dimension | Noveum.ai | Arize | Braintrust |
|---|---|---|---|
| Primary focus | Agent trajectory, trace correctness, behavioral checks | Model output quality, observability, dataset-driven evals | Dataset creation, annotation, expected-output based evals |
| Ground truth required | No (not for most checks) | Often yes for accuracy metrics | Yes — input + expected pairs are central |
| Uses production traces? | Yes — core data source | Yes — but needs labeling for GT metrics | Yes — can generate datasets but must add expected values |
| Hallucination detection | Structural detection (context mismatch, fabricated outputs) | Possible via LLM-judge or human labels | Human annotation required |
| Typical metrics | Prompt adherence, tool usage correctness, context leakage, step validity, drift | Accuracy, precision/recall, calibration, ROUGE/BLEU proxies, embeddings similarity | Accuracy, annotator consensus, dataset coverage |
| Best for | Agent debugging, reliability engineering, production monitoring | Model benchmarking, drift detection, LLM QA | Curating labeled test sets and human-in-the-loop evals |
| Integration complexity | SDK + trace schema + agent instrumentation | SDK + traces + annotation pipelines | Dataset upload + annotation UI / APIs |
Understanding the Fundamentals
Before comparing platforms, it's essential to understand these fundamental concepts that differentiate evaluation approaches.
Trajectory
The ordered sequence of events the agent performs: system prompt, tool calls (with arguments), tool responses, retrievals, intermediate reasoning steps, and final answer.
Evaluating trajectory checks HOW the model arrived at an answer.
Output
Final text/structured response. Output evaluation checks WHAT the model answered against a reference.
Noveum inspects trajectory. Arize/Braintrust are usually focused on output.
When Ground Truth is Essential
When Ground Truth is Optional
These can be detected with robust trace analysis alone (Noveum's approach).
Arize & Others
Support LLM-as-Judge: use an LLM to score outputs with heuristics rather than fixed expected values. This reduces manual labeling but introduces judge noise and calibration needs.
Noveum's Approach
Checks are less subjective: they rely on deterministic schemas, tool contracts, and trace invariants, which are easier to operationalize for agent correctness.
Deep Dive
Explore the specific differences across key dimensions to understand which platform best fits your needs.
Noveum
Practical Examples
See how each platform is typically used in practice with these real-world workflow examples.
No Ground Truth Required
Ingest Traces
Integrate noveum-trace SDK to capture structured trace events from your AI agents in production
Auto-Generate Datasets
Traces are automatically converted to dataset items using AI-generated ETL mapper code
Run Evals & Scorers
112 calibrated scorers analyze each trace, identify errors, and provide detailed reasoning for every issue
NovaPilot Experiments
AI agent uses scores and reasoning to generate fix experiments, running them in simulated environments across generations
Auto-Apply Fixes
Final tested improvements (prompts, models, parameters) are delivered as a PR ready to merge
Small Ground Truth Set
Collect Traces
Gather representative traces from production
Create Labels
Sample a subset and create human-labeled expected answers
Upload Data
Upload input/output/expected to Arize
Run Reports
Run accuracy and calibration reports; use LLM-as-Judge to expand coverage
Model Selection
Use results to choose model/version for production
From Logs
Export Logs
Export logs as CSV (input, model_output, metadata)
Add Expected Values
Use Braintrust UI/API to add expected values and start annotation rounds
Monitor Agreement
Monitor annotator agreement and finalize dataset for model evaluation
Implementation
Understanding the integration requirements and ongoing effort needed for each platform.
Noveum.ai
Moderate
Effort Level
Requires tracing and type instrumentation, but lower annotation pain
Arize / Braintrust
Moderate to High
Requires tracing and type instrumentation, but lower annotation pain
Runs on live traffic; able to surface issues fast
Annotation pipelines and human QA; higher costs for labeling
Powerful for benchmarking and compliance reporting, but annotation overhead is a gating factor
Decision Guide
Use this framework to determine which platform is best for your specific needs.
If You run agentic systems that orchestrate tools
and care about "did the agent behave correctly"
If You need strict accuracy metrics against a known gold standard
(QA correctness, retrieval exactness, labeled tasks)
If You want both
continuous monitoring AND periodic benchmarks
Start monitoring your agents with Noveum.ai today. No ground truth required, no manual labeling overhead.
Honest Assessment
No evaluation approach is universally sufficient. Understanding where each platform excels and falls short helps make the right choice.
Noveum excels at detecting structural and provenance-based errors using complete trace context. It can surface partial correctness, degrees of completeness, and context-supported vs. unsupported claims. However, cases requiring external canonical answers (precise numeric solutions, legal statutes, regulated medical facts) may still need ground truth.
For tasks demanding a single authoritative ground truth (exact math solutions, regulatory compliance assertions matching statute text), organizations often still maintain a gold dataset for auditability.
Noveum's strengths are proportional to trace fidelity. Well-instrumented traces (typed tool-call records, retrieval provenance, token-level context) enable rich scoring; poor traces reduce coverage.
If you cannot or will not instrument your agent to emit detailed traces/spans, Noveum's advantages are limited. This is why Noveum provides ETL pipelines to convert raw traces into clean, enriched dataset items.
Arize is a strong fit for non-LLM / non-agent workloads: classical ML models, tabular models, time-series forecasting, recommendation systems, classification/regression pipelines.
Arize excels at feature distribution drift, population stability index (PSI), embedding drift, and statistical monitoring over long time horizons for ML systems.
In organizations where ML governance frameworks already exist (model cards, dataset lineage, statistical audits), Arize fits naturally.
Note: Noveum is explicitly not designed for traditional ML and does not aim to replace Arize in this space.
Braintrust is optimized for large-scale human review workflows, reviewer management, and annotation QA. Noveum minimizes human labeling and is not an annotation platform.
When regulations or customers require explicitly human-verified labels stored as datasets, Braintrust is often required.
Braintrust-labeled datasets can be reused directly for fine-tuning or supervised learning workflows.
Summary
A quick reference guide to help you choose the right platform for each scenario.
| Scenario | Best Choice |
|---|---|
| Agent misuse, tool errors, hallucinations, partial correctness | Noveum.ai |
| Live production monitoring without labels | Noveum.ai |
| Model comparison without ground truth | Noveum.ai |
| LLM-as-Judge evaluations at scale | Noveum.ai |
| Debugging complex agent flows | Noveum.ai |
| Traditional ML (non-LLM) monitoring | Arize |
| Human annotation & gold-label creation | Braintrust |
| Publishing accuracy metrics with ground truth | Arize / Braintrust |
| Human-reviewed correctness | Braintrust |
Noveum is stronger than Arize/Braintrust for agent and LLM model comparison when ground truth is unavailable. You can run the same agent or prompt against GPT-4, Claude, Gemini, etc., and compare aggregate scores across 112 calibrated scorers (hallucination, completeness, context fidelity, tool correctness, safety, bias, format, etc.) using identical traces and evaluation logic.
Noveum provides first-class LLM-as-judge scoring across its scorer library. It can identify hallucination, partial correctness, accuracy failures, and qualitative degradation directly from traces, without expected outputs. This is not an area where Arize is better.
Expert Guidance
Structured traces (tool events + retrieval provenance) are the foundation for both behavioral checks and for creating labeled datasets later.
Catch structural issues cheaply with Noveum-style checks before investing heavily in labeling.
Keep a curated labeled set (50–500 examples) for calibration of any LLM-judge methods and for regression testing.
Detect likely failures with trajectory checks, then send prioritized samples to human labelers (via Braintrust-style tools) to build GT selectively.
Make it simple to reproduce evaluations across model changes.
Appendix
Missing Required Tool Call
If scenario required bank_api call but no tool event for bank_api appears → alert.
Fabricated Tool Output
If final answer cites a doc id or snippet that wasn't present in retrieved docs → alert.
Context Leakage
If answer includes user data not in current session context → alert.
Premature Answer
If final_answer event occurs before required validation/step events complete → alert.
Accuracy Template
Prompt judge with input + model_output + expected; ask for pass/fail and confidence.
Hallucination Template
Prompt judge with input + model_output + retrievals; ask if any claims are unsupported.
Traces → ETL → Enriched items (goldens): Noveum provides ETL pipelines that take raw traces/spans from production, normalize and enrich them (add retrieval provenance, tool-call typing, tokenized contexts, metadata), and produce clean dataset items. These items can act as goldens for targeted benchmarking, be exported for human annotation, or be used directly as high-quality test cases in automated eval pipelines.
Why this matters: It allows teams to get the best of both worlds: continuous, trace-based monitoring plus the ability to extract and maintain curated golden datasets for audits, regulatory needs, or external benchmark comparisons.
Common Questions
Get answers to the most common questions about choosing between these platforms.
Yes — Noveum can triage and prioritize traces. Use high-confidence structural failure detections to seed a labeled dataset for human review or for Arize benchmarking.
They catch different classes. Noveum catches hallucinations that are structural (claims unsupported by trace/retrievals). Arize/Braintrust detect hallucinations only insofar as annotators or LLM-judges label an answer as hallucinated.
For many production agent teams, yes. Use Noveum for continuous, low-overhead monitoring and Arize/Braintrust for high-quality benchmark reporting and model selection.
Noveum's checks are less subjective: they rely on deterministic schemas, tool contracts, and trace invariants, which are easier to operationalize for agent correctness. LLM-as-Judge introduces judge noise and calibration needs.
Noveum's strengths are proportional to trace fidelity. Well-instrumented traces (typed tool-call records, retrieval provenance, token-level context) enable rich scoring; poor traces reduce coverage. Noveum provides ETL pipelines to help improve trace quality.
For many use cases, yes — especially for detecting structural and behavioral failures. However, for tasks requiring external canonical answers (precise numeric solutions, regulatory compliance), you may still need a small gold dataset for auditability.
See how Noveum.ai can help you understand and improve your AI agents without the overhead of manual labeling.
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