The AI Observability Challenge

Production AI Agents Fail Without Proper Monitoring

B2B companies running AI agents at scale face unique observability challenges. Traditional APM tools weren't designed for LLMs, multi-agent workflows, or AI-specific failure modes.

LLM Behavior Is Invisible

Traditional monitoring can't see inside AI agents. Without LLM-specific tracing, hallucinations, quality degradation, and agent failures go undetected.

Multi-Agent Debugging Is Complex

Production AI agents orchestrate multiple LLM calls, tools, and handoffs. Finding root causes in complex agent workflows takes days without proper observability.

AI Costs Spiral Unexpectedly

Token usage and API costs can spike 10x overnight. Without real-time cost monitoring by model, feature, and user, budget overruns become common.

Quality Degrades Silently

AI agent quality drifts over time. Without continuous automated evaluation, you only discover problems when customers complain.

The Business Impact of Poor AI Observability

Companies without production AI monitoring experience 5x more incidents, 3x longer debugging cycles, and miss early signs of quality degradation. Your AI agents need the same observability as your critical infrastructure.

73%of production AI issues are caught by users, not monitoring
Built for B2B AI Teams

Trusted by Companies Running AI Agents in Production

From customer-facing chatbots to autonomous research agents, Noveum.ai provides the AI observability platform that B2B companies need to monitor, evaluate, and optimize their production AI systems.

AI Engineers & MLOps

Debug complex multi-agent workflows, analyze LLM performance, and optimize AI systems with production-grade tracing and evaluation.

  • ✓End-to-end trace visibility across agents, LLMs, and tools
  • ✓Real-time latency, token usage, and cost analytics
  • ✓Root cause analysis with hierarchical span visualization
Learn more→

Product & Engineering

Ship AI features confidently with automated quality evaluation. Catch regressions before users do with 73+ evaluation scorers.

  • ✓Automated quality scoring with 73+ evaluation metrics
  • ✓Continuous regression detection in production
  • ✓Custom dashboards and real-time alerting
Learn more→

Enterprise & Compliance

Scale AI responsibly with SOC 2 Type II certified infrastructure, complete audit trails, and enterprise-grade security.

  • ✓SOC 2 Type II certified with GDPR support
  • ✓Complete audit trails and PII detection
  • ✓Cost controls and budget management
Learn more→

Works With Any AI Application

Customer Chatbots
Autonomous AI Agents
Support & Operations
The Complete Platform

Monitor. Evaluate. Debug. Optimize.

Noveum.ai provides everything you need to run AI agents in production with confidence. From real-time monitoring to automated fixes.

Monitor

Monitor

Real-time visibility into every agent interaction with traces, spans, and performance metrics.

Evaluate

Evaluate

Automated scoring with 30+ LLM-as-Judge metrics. Know quality before your users do.

Debug

Debug

Drill into any trace to find exactly where things went wrong. No more guesswork.

Optimize

Optimize

Get AI-powered recommendations for prompts, models, and architecture improvements.

Key Features

Trace Visualization

Multi-Agent Support

Cost Analytics

Dataset Builder

30+ AI Scorers

Auto-Fix Suggestions

How Noveum.ai Works

AI Agent Eval Pipeline

Get started with NovaPilot in minutes. Monitor, evaluate, and improve your AI agents continuously.

1

Integrate SDK

Add our Python or TypeScript SDK with a simple decorator. Takes 5 minutes.

2

Start Monitoring

Traces flow automatically. See every LLM call, tool use, and agent decision.

3

Run Evaluations

Score agent quality with our suite of 30+ automated evaluation metrics.

4

Improve Continuously

Use insights to optimize prompts, reduce costs, and ship better agents.

Simple Integration

Add Tracing in Minutes, Not Days

Our SDKs integrate seamlessly with your existing AI stack. Use decorators, callbacks, or context managers - whatever fits your workflow.

  • Python decorators for instant tracing
  • LangChain & LangGraph callback handlers
  • LiveKit STT/TTS wrappers with audio capture
  • Context managers for granular control
View DocumentationGitHub
import noveum_trace
from noveum_trace import trace_llm, trace_agent

# Initialize once
noveum_trace.init(
    project="my-ai-app",
    api_key="your-api-key"
)

# Decorator-based tracing
@trace_llm(model="gpt-4")
def generate_response(prompt: str) -> str:
    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

@trace_agent(agent_id="researcher")
def research_agent(query: str) -> dict:
    results = search_web(query)
    analysis = generate_response(f"Analyze: {results}")
    return {"query": query, "analysis": analysis}
Auto-instrumentation
Zero performance overhead
OpenTelemetry compatible
Noveum Analytics Dashboard
Noveum ScorersNoveum Evaluation Jobs

Monitor all your AI Agents

improve AI Agents today

Noveum.ai helps you monitor, trace, and optimize your AI applications.

Noveum.ai works with any AI framework – LangChain, CrewAI, AutoGen, custom implementations, or direct LLM calls. One dashboard shows everything.

Monitor, Evaluate, Improve Your AI Agents

The control plane for AI agents.

Monitor Everything, Miss Nothing

Monitor Everything, Miss Nothing

Our lightweight SDKs capture every trace and span across your AI agent ecosystem—from simple LLM calls to complex multi-agent workflows. Get complete visibility without performance overhead.

Start Monitoring→
Evaluate with 68+ Advanced Metrics

Evaluate with 68+ Advanced Metrics

NovaEval automatically scores every agent interaction using our comprehensive evaluation framework. Track accuracy, semantic similarity, safety, bias, and custom business metrics in real-time.

View Evaluations→
Improve Automatically with NovaPilot

Improve Automatically with NovaPilot

Our AI engineer analyzes performance data and automatically generates fixes for failing agents. Get detailed reports on model changes, prompt optimizations, and tool improvements—all without human intervention.

Try Auto-Improvement→
Enterprise Ready

Enterprise Ready

Noveum.ai is built for enterprise-scale AI applications, with support for multi-tenant, multi-region deployments and advanced security features.

Contact Sales→
Use Cases

Trusted Across AI Applications

From customer support bots to autonomous research agents, Noveum.ai helps teams ship AI that works.

Customer Support Chatbots

Customer Support Chatbots

Monitor conversation quality, track resolution rates, and ensure your support bot delivers helpful answers every time.

Conversation QualityIntent DetectionResponse Time
Autonomous AI Agents

Autonomous AI Agents

Trace multi-step reasoning chains, monitor tool usage, and debug complex agent workflows with full visibility.

Multi-Step TracingTool MonitoringReasoning Analysis
RAG Pipelines

RAG Pipelines

Evaluate retrieval quality, measure faithfulness to source documents, and optimize chunk strategies for accuracy.

Retrieval QualityFaithfulnessContext Precision
Workflow Automation

Workflow Automation

Monitor automated workflows, track success rates, and get alerts when AI-driven processes fail.

Process MonitoringSuccess RatesError Alerts
Data Analysis Agents

Data Analysis Agents

Track cost per query, monitor accuracy of insights, and optimize token usage for analytical workloads.

Cost Per QueryAccuracy MetricsToken Optimization
Complete Guide

Understanding AI Agent Observability

A comprehensive guide to monitoring, evaluating, and optimizing AI agents in production environments.

What is AI Agent Observability?

AI Agent Observability is the practice of gaining deep visibility into how your AI agents, LLMs, and multi-agent systems behave in production. Unlike traditional application monitoring that tracks HTTP requests and database queries, AI observability requires understanding the unique characteristics of language models—including prompt-response pairs, token usage, latency patterns, and most importantly, the quality and correctness of AI outputs.

For B2B companies running AI agents at scale, observability isn't optional—it's essential for maintaining reliability, controlling costs, and ensuring your AI systems deliver value to customers. Production AI agents can exhibit unpredictable behavior, hallucinate incorrect information, or degrade silently over time without proper monitoring.

End-to-End Tracing

Capture every LLM call, tool interaction, and agent decision in hierarchical traces.

Quality Evaluation

Automatically score outputs for accuracy, relevance, safety, and business-specific criteria.

Performance Monitoring

Track latency, costs, token usage, and error rates across your entire AI infrastructure.

Why AI Observability Matters for Production Systems

Production AI systems face challenges that traditional software doesn't encounter. Without specialized observability, you're essentially flying blind with systems that can fail in subtle, hard-to-detect ways.

Detect Hallucinations Before Users Do

AI agents can confidently produce incorrect information. Automated evaluation catches these errors before they damage customer trust or cause business harm.

Control and Optimize AI Costs

Token usage can spiral unexpectedly. Visibility into per-request costs helps identify optimization opportunities and prevent budget overruns.

Maintain Response Time SLAs

LLM latency varies significantly. Understanding latency patterns helps you meet performance requirements and improve user experience.

Meet Compliance Requirements

Enterprise deployments require audit trails, PII detection, and content filtering. Observability provides the visibility needed for regulatory compliance.

Key Capabilities of a Production AI Observability Platform

A comprehensive AI observability platform should provide end-to-end visibility into your AI systems while being easy to integrate and maintain. Here are the essential capabilities to look for:

01

Hierarchical Trace Visualization

View complete request flows from user input to final response, including all intermediate LLM calls, tool executions, and agent decisions.

02

Multi-Agent System Support

Track interactions between multiple AI agents, understand handoffs, and debug complex orchestration patterns.

03

Automated Quality Evaluation

Score every AI output against accuracy, relevance, safety, and custom business metrics using LLM-as-Judge technology.

04

Cost and Usage Analytics

Break down spending by model, feature, user, or any custom dimension. Identify optimization opportunities and forecast costs.

05

Automated Issue Resolution

Get AI-powered recommendations for fixing issues, with suggested prompt improvements and configuration changes.

06

Actionable Insights

Receive alerts on quality degradation, cost anomalies, and performance issues before they impact users.

Building Production-Ready AI Systems

Moving from prototype to production requires more than just deploying your AI agent. Here's what production-ready AI observability enables:

Real-Time Alerting

Get notified immediately when quality drops, costs spike, or errors increase beyond thresholds.

Scalable Data Ingestion

Handle millions of traces per day without impacting your application's performance.

Security & Compliance

SOC 2 Type II certified with support for GDPR, HIPAA, and enterprise security requirements.

Easy SDK Integration

Integrate in minutes with Python and TypeScript SDKs that support LangChain, LangGraph, CrewAI, and more.

Team Collaboration

Share dashboards, create custom views, and collaborate on debugging across your organization.

Continuous Evaluation

Run automated evaluations on every request to catch issues before they affect users.

Ready to gain complete visibility into your AI agents?

Start Free TrialRead Documentation
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With the world's favorite AI observability platform

Easy integration with your AI stack

Noveum.ai integrates seamlessly with all popular AI frameworks and providers, giving you comprehensive observability across your entire AI pipeline.

Works great with: LangChain, OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Google Cloud (Vertex AI), CrewAI, LangGraph, LlamaIndex, AutoGen, custom SDKs, and more

See all integrations

with the world's favorite AI observability platform

Trusted AI monitoring tools by thousands of developers

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Personalized walkthrough for your use case
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What You'll Get

Live demo of trace visualization and debugging
Custom evaluation setup for your agents
Cost optimization recommendations
Integration guidance for your tech stack
30-minute call

We'll cover your specific needs and show you how Noveum.ai fits your workflow.

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About Noveum.ai — The AI Reliability, Evaluation & Auto-Fixing Platform

Noveum.ai is the leading platform for AI Reliability, LLM Evaluation, AI Agent Monitoring, and automated improvement workflows. As companies deploy AI Agents to power customer service, workflow automation, voice interfaces, RAG search systems, and enterprise copilots, the need for predictable, safe, and efficient AI performance has never been higher. Modern AI Agents frequently hallucinate, skip steps, misunderstand instructions, misuse tools, or generate unpredictable outputs that undermine trust. Noveum.ai solves this across three pillars: Monitoring, Evaluation, and Auto-Fixing.

With a lightweight SDK integration, Noveum captures everything your AI Agent does: prompts, decisions, intermediate reasoning, tool calls, token usage, context retrievals, model behaviors, and workflow execution traces. This creates a complete AI observability layer, giving companies unprecedented insight into how their agents behave internally. From cost spikes to hallucination risks, from broken workflows to reasoning failures, Noveum identifies issues before customers experience them.

The platform also includes an advanced evaluation engine powered by 67 specialized scoring functions, covering accuracy, factuality, hallucination detection, RAG retrieval quality, role adherence, conversation health, structured output validation, safety, bias, and compliance. Noveum.ai generates detailed reasoning and provides clear explanations behind every score — helping teams see why an issue happened, not just where.

Finally, Noveum assists with AI agent auto-fixing by running controlled experiments in a sandbox. It tests prompt variations, model alternatives, parameter adjustments, and tool-call corrections to find the best configuration. After evaluation, Noveum provides improvement recommendations and a full report on how to fix issues. Teams can apply improvements manually or enable auto-fixing workflows that automatically update the agent logic and push PRs.

Noveum.ai is the world's first end-to-end AI Agent Maintenance Platform, helping teams achieve reliability, cost efficiency, and continuous performance improvement without hiring a large ML engineering team. This footer provides a comprehensive reference to how Noveum.ai works and includes deep explanations of all evaluation scorers used to assess AI quality.

Why AI Reliability Matters for Modern Teams

AI Agents are becoming the backbone of digital operations — answering customer questions, triaging support tickets, qualifying leads, routing tasks, summarizing documents, analyzing data, and automating workflows. But unlike traditional software, AI is inherently probabilistic. Even well-designed agents will:

  • hallucinate facts
  • misunderstand instructions
  • misuse tools
  • return irrelevant answers
  • generate inconsistent formats
  • exceed token budgets
  • produce partial responses
  • violate compliance rules
  • degrade over time without monitoring

Unreliable AI leads to increased support burden, lost revenue, compliance risks, poor customer experience, and rising operational costs. Noveum.ai provides the infrastructure needed to measure, monitor, and maintain AI Agent quality at scale.

Noveum Monitoring — Complete AI Observability

Noveum captures all agent behaviors in real time:

  • Full prompt & response history
  • Tool call inputs and outputs
  • Token usage tracking
  • Cost attribution per action
  • System and user message flows
  • Context retrieval logs
  • Branching workflow behavior
  • Intermediate reasoning
  • Agent chain-of-thought traces
  • Latency analysis
  • Failure modes and exceptions

The Agent Visualizer produces a pre-order execution graph showing how the AI Agent reasons and acts across steps. This visual monitoring layer enables developers, product managers, and operations teams to understand the internal logic of their AI Agents and diagnose complex failure patterns.

Noveum Evaluation — Scoring AI Performance with 67 Specialized Scorers

Noveum.ai includes the most comprehensive evaluation library available for enterprise AI Agents. Every scorer has been designed to evaluate a different dimension of AI output quality — from hallucination detection and factual grounding to conversation health, safety compliance, RAG retrieval quality, structured formatting, and system performance.

Each evaluation produces:

  • numeric score
  • natural-language reasoning
  • step-by-step justification
  • references to context used
  • recommendations for improvement
Full Scorer Library With Explanations (All 67 Scorers)
1. Accuracy, Matching & Semantic Quality Scorers
  • ExactMatchScorer
    Checks if the AI output perfectly matches the expected answer. Ideal for classification or deterministic tasks.
  • AccuracyScorer
    Measures the proportion of correct responses across a dataset — essential for baseline LLM quality benchmarking.
  • MultiPatternAccuracyScorer
    Supports multiple correct output forms. Useful for flexible workflows where several answers are acceptable.
  • F1Scorer
    Balances precision and recall to measure how accurately the AI captures all important elements.
  • BLEUScorer
    An n-gram matching metric for evaluating text similarity. Used broadly in summarization and generation tasks.
  • ROUGEScorer
    Measures recall overlap with reference text, especially useful for long-form content evaluation.
  • LevenshteinSimilarityScorer
    Calculates edit distance between expected and generated text, identifying near-matches efficiently.
  • SemanticSimilarityScorer
    Uses embedding-based comparisons to assess meaning similarity, not just surface text similarity.
  • InformationDensityScorer
    Scores how substantive and information-rich an answer is, identifying fluff or low-density output.
  • ClarityAndCoherenceScorer
    Evaluates readability, logical structure, and flow — vital for customer-facing AI responses.
  • AnswerCompletenessScorer
    Checks whether the AI addressed every part of the prompt completely.
  • QuestionAnswerAlignmentScorer
    Ensures direct alignment between user question and generated answer.
  • TechnicalAccuracyScorer
    Validates correctness for domain-specific answers in finance, engineering, legal, or medical tasks.
  • TerminologyConsistencyScorer
    Ensures specialist terminology is used appropriately and consistently in AI responses.
2. Relevance, Faithfulness & Hallucination Scorers
  • AnswerRelevancyScorer
    Measures how relevant the AI's response is to user intent.
  • FaithfulnessScorer
    Checks whether the AI stays grounded in provided context without adding unsupported claims.
  • FactualAccuracyScorer
    Identifies factual inaccuracies by comparing output with known truths.
  • HallucinationDetectionScorer
    Detects fabricated or invented information. One of the most important scorers for enterprise safety.
  • ConflictResolutionScorer
    Identifies contradictions in the AI's reasoning.
  • ContextPrioritizationScorer
    Ensures the AI selects the most important context pieces when generating a response.
  • ContextFaithfulnessScorerPP
    Validates that the answer is grounded in provided documents.
  • ContextGroundednessScorer
    Checks if every important claim is supported by context.
  • ContextCompletenessScorer
    Measures whether the AI uses enough context to form a complete answer.
  • ContextConsistencyScorer
    Ensures consistency with reference material across statements.
3. RAG (Retrieval-Augmented Generation) Scorers
  • ContextualPrecisionScorer
    Evaluates relevance of retrieved context snippets used in the final answer.
  • ContextualRecallScorer
    Measures how much relevant content the AI included from the available context.
  • RAGASScorer
    Industry-standard RAG quality scoring across groundedness, relevance, and answer quality.
  • ContextualPrecisionScorerPP
    Enhanced precision evaluation for more complex retrieval chains.
  • ContextualRecallScorerPP
    Advanced recall scoring that checks deeper semantic relevance.
  • RetrievalF1Scorer
    Harmonic mean of precision and recall for retrieval effectiveness.
  • RetrievalRankingScorer
    Evaluates document ranking quality for RAG pipelines.
  • RetrievalDiversityScorer
    Ensures retrieval results include diverse, non-redundant knowledge sources.
  • AggregateRAGScorer
    Provides a combined score summarizing RAG performance.
  • RAGAnswerQualityScorer
    Evaluates whether the final answer is high-quality and well-grounded.
4. Conversation & Role Behavior Scorers
  • ConversationRelevancyScorer
    Checks turn-by-turn relevance in multi-turn dialogues.
  • ConversationCompletenessScorer
    Ensures the entire user intent has been addressed in the conversation.
  • RoleAdherenceScorer
    Ensures the agent sticks to its assigned role and persona.
  • ConversationalMetricsScorer
    Composite conversational quality scorer measuring flow, engagement, and relevance.
5. Safety, Bias & Harm Prevention Scorers
  • AnswerRefusalScorer
    Detects whether AI refuses unsafe or disallowed tasks appropriately.
  • ContentModerationScorer
    Flags potentially harmful, explicit, or policy-violating content.
  • ContentSafetyViolationScorer
    Scores violations of safety, compliance, or governance guidelines.
  • ToxicityScorer
    Evaluates presence of insulting, aggressive, or harmful language.
  • IsHarmfulAdviceScorer
    Detects dangerous recommendations — crucial for sensitive domains.
  • NoGenderBiasScorer
    Ensures fair, unbiased responses related to gender.
  • NoRacialBiasScorer
    Evaluates outputs for racial bias or stereotyping.
  • NoAgeBiasScorer
    Checks for age-related prejudice in AI responses.
  • CulturalSensitivityScorer
    Ensures the AI respects cultural norms and avoids offensive phrasing.
6. Structured Output & Format Scorers
  • IsJSONScorer
    Validates whether responses follow correct JSON formatting.
  • IsEmailScorer
    Checks for valid email generation structure.
  • ValidLinksScorer
    Ensures URLs in the output are properly formatted and valid.
7. Multi-Judge & Deep Reasoning Scorers
  • PanelOfJudgesScorer
    Uses multiple LLM 'judges' to provide averaged evaluations for robust scoring.
  • SpecializedPanelScorer
    Domain-specific multi-judge scorer for technical or expert evaluations.
  • GEvalScorer
    A generalized evaluation approach measuring reasoning, structure, and coherence.
  • AgentScorers
    Bundle of agent-specific evaluations optimized for multi-step AI workflows.
8. Pipeline & System-Level Scorers
  • QueryProcessingEvaluator
    Evaluates how accurately the system interprets and processes user queries.
  • RetrievalStageEvaluator
    Scores the retrieval portion of a RAG pipeline.
  • RerankingEvaluator
    Evaluates how well documents are reranked for relevance.
  • RAGPipelineEvaluator
    Full end-to-end scoring for retrieval + ranking + generation.
  • PipelineCoordinationScorer
    Evaluates coordination of pipeline components across stages.
9. Latency, Resource & Error Scorers
  • LatencyAnalysisScorer
    Measures latency and identifies bottlenecks across the AI execution path.
  • ResourceUtilizationScorer
    Assesses token efficiency, compute usage, and cost impact.
  • ErrorPropagationScorer
    Detects how errors propagate across multi-step agent workflows.
10. Knowledge & Memory Scorers
  • KnowledgeRetentionScorer
    Measures whether an AI preserves key information across turns or steps.
Noveum's Auto-Fixing Engine

After evaluating agents using all scorers above, Noveum automatically analyzes failures and runs improvement experiments such as:

  • prompt optimization
  • model selection for better cost-performance
  • parameter tuning
  • workflow adjustments
  • tool-call corrections

The system then provides a full recommendation report or can automatically generate pull requests to update your agent logic.

AI Cost Optimization With Noveum

Noveum identifies token-heavy prompts, inefficient retrieval patterns, expensive models, and redundant workflow steps. It analyzes cost per generation step and suggests cheaper, higher-performing alternatives.

Companies typically see 20-50% reduction in AI operational cost after implementing Noveum's recommendations.

Noveum.ai — The Future of AI Agent Reliability

As companies scale AI Agents across functions, Noveum provides the necessary foundation to ensure accuracy, reliability, compliance, performance, and cost efficiency. We provide the tooling needed to maintain AI Agents, prevent drift, and keep behavior trustworthy — automatically.