7 Best AI Agent Observability Platforms in 2025

If you’re searching for the best AI agent observability platforms, chances are your agents are already running in production.

As of 2025, too many teams are deploying agents without a clear way to see how they behave.

But visibility separates small, fixable errors from failures that cost time, money, and trust. 

And once agents go off track, you often realize it only when the damage is done.

Observability isn’t optional anymore.

It keeps your AI agents accurate, accountable, and reliable at scale.

This guide’ll look at 7 AI agent observability platforms that help teams track behavior in real time and prevent mistakes before they spread.

TL;DR: Best AI Agent Observability Platforms Overall

• Best for turning AI agent data into role-based dashboards and insights: Upsolve 
  
  

• Best for detecting model drift and performance issues: Arize AI 
  
  

• Best for ensuring clean, reliable data pipelines: WhyLabs 
  
  

• Best for tracking ML experiments and comparing model runs: Weights & Biases (W&B) 
  
  

• Best for explainability, bias detection, and regulatory compliance: Fiddler AI 
  
  

• Best for debugging LLM agents with trace visualization: Langfuse 
  
  

• Best for lightweight usage monitoring and quick setup: Helicone 

What is AI Agent Observability?

AI agent observability is the ability to see, understand, and analyze everything an AI agent does in real time. 

It reveals how agents make decisions, how fast they respond, and whether their outputs are accurate.

Without it, AI agents become black boxes, making it difficult to trust or optimize their actions.

Observability focuses on three critical areas:

• Performance metrics: Track speed, accuracy, and response quality.
  
  

• Behavior tracking: Map decision flows and detect biases or inefficiencies.
  
  

• Error analysis: Catch and fix issues instantly before they escalate.

For LLM-powered agents, observability is vital since their outputs can be unpredictable and spread across multiple workflows. 

Proper monitoring ensures transparency, accountability, and reliable performance at scale.

Key Features to Look for in an AI Agent Observability Platform

The most effective platforms include:

1. Real-time monitoring: Track latency, accuracy, and responses instantly to catch issues before they grow.

2. Analytics and dashboards: Visualize agent behaviors, decision paths, and workflow performance. 

3. Root cause analysis: Understand why errors occur and resolve them quickly with clear decision-flow mapping.

4. Natural language queries: It should allow teams to explore data and filter results simply by asking questions, making observability more accessible.

5. Scalability and integrations: Support larger workloads while connecting seamlessly with enterprise systems and APIs.

6. Security and compliance: Protect sensitive data with encryption and audit-ready controls.

7. Ease of use: Upsolve simplifies adoption with embeddable dashboards, intuitive UI, and minimal setup.

Upsolve combines observability with embedded analytics, helping teams monitor agents in real time while delivering actionable insights directly inside their product.

How Did We Choose the Best Agent Observability Platform?

When evaluating AI agent observability platforms in 2025, we focused on six essentials that determine whether a tool can deliver reliability, transparency, and scalability:

• Real-time visibility into agent behavior, latency, accuracy, and performance.
  
  

• Clear dashboards that show decision paths, reasoning steps, and workflow efficiency.
  
  

• Root cause analysis to detect and fix errors quickly.
  
  

• Scalability to support enterprise workloads with smooth API and LLM integrations.
  
  

• Strong security standards and compliance with data regulations.
  
  

• Ease of use through developer-friendly APIs, SDKs, and intuitive dashboards.

These factors allowed us to compare platforms on the same scale and highlight strengths and weaknesses.

7 Best AI Agent Observability Platforms in 2025

Here’s a detailed look at each tool. 

Today’s observability platforms give businesses clarity and control over complex AI systems, from tracking performance to debugging workflows and ensuring compliance. Based on the evaluation criteria outlined earlier, here are seven of the strongest options to consider in 2025.

Best AI Agent Observability Platforms: Quick Comparison Table

1. Upsolve 

Upsolve brings a unique angle to AI agent observability by focusing on how data is delivered to end users. 

Instead of just tracking performance metrics, it transforms agent behavior into clear, role-based dashboards that make insights actionable across teams. 

With real-time monitoring, natural language queries, and embedded analytics, Upsolve helps businesses understand, debug, and optimize AI agents while keeping information accessible to technical and non-technical users.

Key Features:

• Role-based dashboards that adapt insights for product managers, sales leads, or finance teams.
  
  

• Natural language queries so users can explore agent behavior without SQL or complex filters.
  
  

• Embedded analytics that integrate directly into products for customer-facing observability.
  
  

• Decision path visualization to see how agents arrived at a specific output.
  
  

• Data quality checks that flag anomalies before they impact agent performance.
  
  

• Customizable themes and integrations so dashboards match enterprise systems and workflows.

What makes it a good agent observability tool?

• Fast dashboards, embedded BI, and natural language insights.

• Fast and cost-effective way to build customer-facing analytics features.

• User-friendly dashboards that enhance the experience for end users.

• Enterprise-level analytics, without needing a dedicated team.

• Comprehensive BI solution covering all analytics needs in one platform.

Where could it improve?

• Complex analysis may require multiple iterations to get accurate results.

• Rapid updates mean users must frequently catch up on new features.

• Extensive configuration options can be overwhelming and take time to learn.

Pricing & Plans

• Growth – $1,000+/mo
  
  Basic observability through embedded dashboards.
  
  50 tenants included, CSV/PDF exports, and app embedding.
  
  

• Professional – $2,000+/mo
  
  Adds AI-powered insights into agent behavior.
  
  Unlimited dashboards, scheduled reports, and usage analytics.
  
  

• Enterprise – Custom
  
  Full-scale observability for complex agent workflows.
  
  Unlimited tenants, advanced integrations, SSO, compliance.

Read the complete Upsolve Pricing!

Best For: 

Enterprises and scaling businesses that need comprehensive AI agent monitoring and optimization.

2. Arize AI

Arize AI goes beyond surface-level metrics by offering deep visibility into how models perform in real-world environments. 

Instead of simply detecting drift, it helps teams understand why drift is happening, making AI troubleshooting proactive rather than reactive. 

Its explainability-first approach ensures that both technical and non-technical stakeholders can trace outcomes back to inputs and decisions, fostering trust in AI across the organization. 

Key Features

• Drift Detection Across Dimensions: Real-time alerts when model predictions diverge from expected outcomes or training data distributions.
  
  

• Explainability Tools: Feature importance, prediction breakdowns, and bias analysis help teams understand why a model behaves the way it does.
  
  

• Role-Based Dashboards: Tailored views for data scientists, compliance officers, and business stakeholders, ensuring insights are accessible and relevant.
  
  

• Fairness & Bias Auditing: Built-in bias detection tools highlight disparities across demographics or cohorts.
  
  

• Flexible Integrations: Works with leading ML platforms, pipelines, and data warehouses, ensuring smooth adoption without overhauling infrastructure.
  
  

• Collaboration-Friendly Workflows: Shared dashboards and annotation tools allow cross-functional teams to debug, discuss, and resolve issues in context.

What Makes It a Strong Observability Tool?

• Deep focus on explainability rather than just monitoring numbers.
  
  

• Bias and fairness checks make it suitable for regulated industries.
  
  

• Role-based dashboards ensure each team member sees actionable insights.
  
  

• Real-time drift detection keeps models aligned with shifting data patterns.
  
  

• Integrates easily into existing ML pipelines, reducing friction.
  
  

Where Could It Improve?

• Latency and custom instrumentation issues in certain cases affect the responsiveness and flexibility of the platform.

• Learning curve can be steep for beginners, as some advanced features require deeper expertise and the documentation may feel overwhelming

• API access is limited, making it harder to integrate Arize AI’s features into custom workflows or leverage them through packages.

Pricing may feel steep for small teams or early-stage startups.

• Requires data science maturity—non-technical users may need guidance during onboarding.
  
  

• Advanced features can feel overwhelming without a structured adoption plan.

Pricing & Plans

• AX Free – $0/month
  
  

  • Core model monitoring
    
    

  • Suitable for individuals and small teams
    
    

• AX Pro – Starts at $600/year per seat ($50/month)
  
  

  • Drift detection, advanced dashboards, explainability
    
    

  • Designed for scaling teams needing collaboration
    
    

• Enterprise – Custom Pricing
  
  

  • Full-scale monitoring with advanced compliance, integrations, and large-scale data handling

Best For:

Enterprises and regulated industries need trustworthy, explainable, and bias-aware AI monitoring.

3. WhyLabs

WhyLabs is built around a simple idea — AI is only as good as the data it learns from.

It doesn’t just look at the final results. Instead, it dives deep into the data pipelines that power the AI models.

This way, every dataset stays accurate, timely, and reliable, helping teams trust their workflows.

With continuous monitoring, anomaly detection, and integrations with top data platforms, WhyLabs prevents costly errors before they happen.

Its enterprise-grade scalability makes it a go-to solution for data-driven businesses that depend on flawless AI operations.

Key Features

• Experiment Logging – Logs hyperparameters, datasets, and results in a unified workspace to ensure reproducibility and transparency.
  
  

• Collaboration Tools – Enables real-time sharing of dashboards, charts, and notes so distributed teams can stay aligned.
  
  

• Scalable Storage – Provides secure, cloud-based storage that grows with experiments while maintaining data integrity and accessibility.
  
  

• Framework Integrations – Seamlessly connects with TensorFlow, PyTorch, and Scikit-learn, reducing friction in machine learning workflows.
  
  

• Artifact Versioning – Manages models and datasets with complete version control to support consistent experimentation across teams.
  
  

• Custom Visualizations – Builds advanced charts and visual reports for better interpretation of experiment outcomes and metrics.
  
  

• API Access – Offers flexible API endpoints that allow teams to integrate tracking into existing ML pipelines.

What Makes It a Strong Observability Tool?

•  Centralized logging improves reproducibility of experiments across multiple teams, making results easier to track and share.
  
  

• It integrates seamlessly with popular ML frameworks, helping reduce workflow friction and speed up processes.
  
  

• With team collaboration features, distributed research and experimentation become more coordinated and efficient.
  
  

• Visualization tools turn complex metrics into clear, easy-to-understand dashboards for better decision-making.
  
  

• Artifact versioning keeps datasets clean and experiment tracking reliable, ensuring consistency over time.

Where Could It Improve?

• Custom monitors and metrics are difficult to build, with code issues that could be better addressed through improved documentation or FAQs.

• Limited user reviews make it harder to assess real-world performance and trust in the platform’s maturity and support.

• Setup complexity around sensitivity requires trial and error, and certain actions can only be performed through API calls, not the UI.

Pricing & Plans

• Growth – $1,000+/mo
  
  Basic data monitoring with anomaly detection.
  
  50 tenants included, CSV/PDF exports, app embedding.
  
  

• Professional – $2,000+/mo
  
  Adds advanced insights and integration tools.
  
  Unlimited dashboards, scheduled reports, usage analytics.
  
  

• Enterprise – Custom
  
  Full-scale observability for enterprise data systems.
  
  Unlimited tenants, advanced integrations, SSO, compliance.
  
  

Best For

Teams needing scalable data quality monitoring to ensure reliable AI model performance.

4. W&B (Weights & Biases)

W&B is a top platform for experiment tracking and ML observability. It’s widely used by data science teams.

Its collaboration tools help teams manage experiments, compare results, and monitor training in real time.

The platform integrates with popular ML frameworks to streamline model development. It also boosts transparency and reproducibility.

With enterprise-grade scalability, W&B is trusted by data-driven businesses.

Key Features

• Experiment Logging – Logs hyperparameters, datasets, and metrics across experiments for easy reproducibility and comparison.
  
  

• Collaboration Tools – Enables real-time sharing of results, charts, and notes among distributed research teams.
  
  

• Artifact Versioning – Tracks datasets and models with version control to avoid duplication and confusion.
  
  

• Framework Integrations – Connects seamlessly with PyTorch, TensorFlow, and Scikit-learn to simplify machine learning workflows.
  
  

• Custom Visualizations – Allows teams to create advanced, shareable visualizations for performance tracking and reporting.
  
  

• Scalable Storage – Provides cloud-based storage for managing large experiment histories securely and efficiently.
  
  

• API Access – Offers API endpoints for flexible integration into existing pipelines and workflows.
  
  

• Model Monitoring – Tracks model performance, drift, and anomalies in real time to ensure reliability and accuracy.

What Makes It a Strong Observability Tool?

•  Centralized logging supports reproducibility across multiple experiments.
  
  

• Rich framework integrations reduce setup friction for ML teams.
  
  

• Visualization tools make complex metrics easier to understand.
  
  

• Collaboration features support distributed research and communication.
  
  

• Artifact versioning ensures consistent and reliable experimentation history.
  
  

Where Could It Improve?

• Documentation for basic functionalities is lacking, making it difficult for users to find and use essential features.

• Server lag in the online approach can affect performance, causing delays during critical operations.

• Missing features like global normalization settings and window management options reduce flexibility and usability.

Pricing & Plans

• Growth – $1,000+/mo
  
  Basic observability through embedded dashboards. 50 tenants included, CSV/PDF exports, app embedding.
  
  

• Professional – $2,000+/mo
  
  Adds AI-powered insights into agent behavior. Unlimited dashboards, scheduled reports, usage analytics.
  
  
  

• Enterprise – Custom
  
  Full-scale observability for complex agent workflows. Unlimited tenants, advanced integrations, SSO, compliance.

Best For: 

Teams and enterprises needing experiment tracking, collaboration, and reproducibility in machine learning workflows.

5. Fiddler AI

Fiddler AI focuses on explainability in AI systems, offering observability tools tailored for regulated industries and compliance-heavy organizations. 

It helps teams understand, monitor, and debug models by revealing the reasoning behind decisions. 

By emphasizing fairness, bias detection, and regulatory alignment, Fiddler AI supports transparency while maintaining performance insights, making it a powerful ally for organizations with accountability at their core.

Key Features

• Explainability Workflows – Guides users with step-by-step insights to better interpret model predictions in real scenarios.
  
  

• Bias Attribution Engine – Pinpoints which data segments and features are contributing to fairness issues for targeted fixes.
  
  

• Adaptive Drift Alerts – Learns from historical patterns to send smarter alerts that focus on real, actionable changes.
  
  

• Governance Scorecards – Provides live dashboards to track compliance, fairness, and model health across teams easily.
  
  

• Scenario Simulation Studio – Lets teams test different input scenarios to see how changes impact model results before deployment.
  
  

• Federated Learning Support – Enables secure, distributed model training while preserving data privacy across multiple locations.

What Makes It a Strong Observability Tool?

• Deep explainability clarifies model decisions beyond metrics alone.
  
  

• Bias checks ensure fairness across sensitive data categories.
  
  

• Compliance tools help meet strict industry regulations effectively.
  
  

• Drift monitoring maintains model accuracy in production use.
  
  

• Custom integrations enable seamless enterprise-level adoption quickly.

Where Could It Improve?

• Usability can be difficult for users new to AI, AIOps, and AI monitoring, requiring more intuitive onboarding or tutorials.

• A free version with limited features would help new users explore the tool before fully committing.

Pricing & Plans

• Growth – $1,000+/mo
  
  Basic observability through embedded dashboards. 50 tenants included, CSV/PDF exports, app embedding.
  
  

• Professional – $2,000+/mo
  
  Adds AI-powered insights into agent behavior. Unlimited dashboards, scheduled reports, usage analytics.
  
  

• Enterprise – Custom
  
  Full-scale observability for complex agent workflows. Unlimited tenants, advanced integrations, SSO, compliance.
  
  

Best For: 

Enterprises in regulated industries prioritizing explainability, compliance, and fairness in AI workflows.

6. Langfuse

Langfuse is built for monitoring large language models (LLMs).

It offers specialized tools to visualize and debug conversational AI workflows.

By tracking each request and response, it helps teams understand model decisions.

This improves how models perform over time.

With trace visualizations, developers can see how the AI works step by step.

Flexible debugging tools make it easier to fix issues.

Langfuse also gives insights into LLM behavior, helping teams refine and scale their AI applications confidently .

Key Features

• Trace Visualization – Displays the full request-response chain to clarify how LLMs reach outputs.
  
  

• Prompt Debugging – Analyzes prompts and responses to improve consistency and reduce hallucinations.
  
  

• Usage Analytics – Tracks token usage and cost metrics for better resource planning.
  
  

• Error Monitoring – Identifies failed or degraded responses, enabling faster debugging.
  
  

• Version Tracking – Keeps records of prompt versions to compare effectiveness across updates.
  
  

• Integrations – Works seamlessly with OpenAI, Anthropic, and Hugging Face pipelines.
  
  

• Custom Dashboards – Allows teams to build dashboards that highlight key LLM performance metrics.
  
  

What Makes It a Strong Observability Tool?

• Trace visualizations clarify request-response chains quickly.
  
  

• Prompt debugging reduces LLM errors and inconsistencies.
  
  

• Usage analytics keep costs predictable for enterprises.
  
  

• Error monitoring accelerates troubleshooting in real time.
  
  

• Integration options simplify adoption across AI pipelines.

Where Could It Improve?

• Niche focus on LLMs limits wider usage.
  
  

• Fewer compliance tools than competitors.
  
  

• Limited offline usability in secure environments.
  
  

• May overwhelm teams new to observability tools.
  
  

• Still maturing compared to larger platforms.
  
  

Pricing & Plans

• Growth – $1,000+/mo
  
  Basic observability through embedded dashboards. 50 tenants included, CSV/PDF exports, app embedding.
  
  

• Professional – $2,000+/mo
  
  Adds AI-powered insights into agent behavior. Unlimited dashboards, scheduled reports, usage analytics.
  
  

• Enterprise – Custom
  
  Full-scale observability for complex agent workflows. Unlimited tenants, advanced integrations, SSO, compliance.
  
  

Best For:

Teams building and scaling LLM-powered applications that require deep visibility and performance optimization.

7. Helicone

Helicone is a lightweight AI observability tool designed for quick adoption and straightforward monitoring. 

It focuses on helping teams track API usage, monitor model performance, and manage costs efficiently. 

By offering simple logging and visualization features, Helicone is well-suited for startups and smaller organizations that need transparency without the overhead of complex observability platforms.

Key Features

• API Logging – Captures every request and response to ensure transparent monitoring of AI workflows.
  
  

• Usage Tracking – Tracks API calls, token consumption, and associated costs for budget control.
  
  

• Performance Metrics – Monitors response times and error rates to evaluate system reliability.
  
  

• Lightweight Integration – Simple SDKs and plugins enable fast deployment without complex setup.
  
  

• Custom Dashboards – Lets users design visual reports that highlight usage and performance metrics.
  
  

• Collaboration Support – Provides team-based access to logs and dashboards for shared insights.
  
  

• Export Options – Supports CSV and PDF export for sharing data outside the platform.
  
  

What Makes It a Strong Observability Tool?

• Lightweight design makes adoption fast for startups and small teams.
  
  

• Usage tracking enables better control over API costs effectively.
  
  

• Performance metrics ensure reliable and predictable system behavior.
  
  

• Collaboration features enhance visibility across technical and non-technical stakeholders.
  
  

• Export options simplify sharing observability data externally when needed.
  
  

Where Could It Improve?

• Numerous alternatives make it harder to choose Helicone, as competing solutions offer similar or better functionality.

• Custom implementation of an LLM proxy is challenging, requiring significant effort on frameworks like Axflow.

• Upload scanning takes too long, affecting the overall efficiency of the platform.

Pricing & Plans

• Growth – $1,000+/mo
  
  Basic observability through embedded dashboards. 50 tenants included, CSV/PDF exports, app embedding.
  
  

• Professional – $2,000+/mo
  
  Adds AI-powered insights into agent behavior. Unlimited dashboards, scheduled reports, usage analytics.
  
  

• Enterprise – Custom
  
  Full-scale observability for complex agent workflows. Unlimited tenants, advanced integrations, SSO, compliance.

Best For: 

Startups and small teams seeking lightweight AI monitoring with cost transparency and simple setup.

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How to Choose the Right AI Agent Observability Platform

The right platform depends on how your team uses AI agents.

A lightweight tool may suit a startup testing a few LLMs, while enterprises running mission-critical workflows need advanced, enterprise-grade solutions.

• Size of your team: Smaller teams need a simple setup. Larger teams need dashboards, compliance, and collaboration.
  
  

• Workflow complexity: Basic apps work with simple monitoring. Multi-agent or autonomous workflows require full decision-flow visibility.
  
  

• Budget and scalability: Startups prefer flexible pricing. Enterprises need scalable options for heavy workloads.
  
  

• Depth of analytics needed: Some tools track data quality, others provide detailed decision paths and workflow optimization.

Upsolve combines monitoring, visualization, and workflow insights for businesses scaling LLM agents in one platform, reducing the need for multiple tools.

Conclusion

AI agents now run critical business workflows, but they remain black boxes without observability.

Teams cannot trace decision paths, spot performance issues early, or prevent errors from scaling.

Upsolve closes that gap by combining observability with embedded analytics:

• Unified dashboards that replace fragmented monitoring tools.
  
  

• Decision-path visibility to explain how and why agents act.
  
  

• AI-powered recommendations that turn monitoring into workflow improvement.
  
  

While most platforms focus on narrow use cases like model drift or data quality, Upsolve delivers full visibility across agents, data, and workflows in one system. 

This makes it especially valuable for enterprises scaling LLM-powered agents where reliability, transparency, and efficiency are non-negotiable.

Chat with us and try a live demo!

FAQs

What is AI agent observability?

AI agent observability tracks and explains how AI agents perform, helping improve reliability and decision-making.

Why is AI observability important in 2025?

It ensures AI agents are accurate, compliant, and trustworthy, reducing risks and boosting performance.

How is observability different from monitoring?

Monitoring tracks metrics; observability explains why agents act a certain way, helping with debugging and scaling.

Which businesses need AI agent observability?

Startups and enterprises in finance, healthcare, and e-commerce benefit most from reliable and transparent AI workflows.

How do pricing models vary across platforms?

Some platforms offer free tiers, others subscriptions, while Upslove uses flexible usage-based pricing for all team sizes.

What makes Upslove stand out from competitors?

Upslove offers complete, real-time observability with deep insights and optimization tools for scaling AI effectively .