# Best AI Agent Observability Software *By [Tian Lin](https://research.g2.com/insights/author/tian-lin)* AI agent observability platforms are software tools that give engineering and data teams end-to-end visibility into the behavior, performance, and reliability of AI agents operating in production. As organizations deploy agents that orchestrate large language models (LLM) with external tools, memory, retrieval systems, and multi-step reasoning workflows, the complexity and non-deterministic nature of these systems make traditional monitoring approaches insufficient. AI agent observability platforms are purpose-built to address this gap, providing the tracing, evaluation, and alerting capabilities teams need to detect, diagnose, and resolve issues across every layer of an agentic system. AI agent observability platforms create value by closing the gap between AI deployment and AI accountability. They reduce the time required to identify and resolve production issues, enable continuous quality evaluation without manual review at scale, and give business and technical leaders the confidence to expand AI initiatives, knowing that performance is being monitored and measured. Rather than replacing engineering judgment, these platforms extend it, surfacing the signals that would otherwise require hours of manual investigation. Organizations use AI agent observability platforms to understand not just what an agent produced, but why it produced it—tracing the full chain of reasoning, tool calls, retrieval steps, and model interactions that led to a given output. This level of visibility is essential for identifying failure modes such as hallucinations, prompt drift, degraded retrieval quality, runaway token costs, and silent performance regressions that would otherwise go undetected until they impact end users or business outcomes. These platforms are used primarily by AI engineers and machine learning (ML) engineers who need to debug and optimize agent behavior, MLOps and platform engineers responsible for maintaining AI systems at scale, data teams ensuring that the inputs feeding agents are accurate and reliable, and governance and compliance teams that require audit trails and transparency into how AI systems arrive at decisions. They are deployed across industries where agentic AI systems are moving from pilot to production and where reliability and trust are prerequisites for continued investment. Unlike traditional application performance monitoring tools, which capture infrastructure and code-level telemetry, AI agent observability platforms are designed for the unique characteristics of AI systems: non-deterministic outputs, multi-step reasoning chains, prompt and context sensitivity, and quality dimensions that cannot be assessed through conventional error rates or latency metrics alone. They apply AI-native evaluation methods such as LLM-as-judge scoring, semantic similarity checks, and deterministic rule-based evaluations to assess output quality continuously and at scale. They are equally distinct from data observability platforms, which focus on the health and reliability of data pipelines, warehouses, and BI systems. While data observability ensures that the inputs feeding an AI system are accurate and timely, it does not monitor what the agent does with those inputs—the reasoning, tool calls, model behavior, and outputs that AI agent observability platforms are specifically built to surface. These platforms integrate with systems such as [large language models (LLMs)](https://www.g2.com/categories/large-language-models-llms), [cloud data warehouses](https://www.g2.com/categories/data-warehouses), [vector databases](https://www.g2.com/categories/vector-databases), [data observability platforms](https://www.g2.com/categories/data-observability), and [MLOps tools](https://www.g2.com/categories/mlops), positioning them as the monitoring and evaluation layer that makes production AI systems trustworthy, explainable, and operationally sustainable. To qualify for inclusion in the AI Agent Observability category, a product must: - Provide end-to-end tracing of multi-step AI agent workflows, including LLM calls, tool invocations, retrieval steps, and intermediate reasoning states - Support automated evaluation of agent outputs using methods such as LLM-as-judge, rule-based checks, or custom evaluators - Monitor agent performance in production, including token usage, latency, cost attribution, and error rates - Alert teams to quality degradations, behavioral regressions, or system failures in agentic workflows - Address the non-deterministic nature of AI systems, not solely traditional application or infrastructure metrics - Support deployment in production environments, not only offline testing or pre-release evaluation ## Category Overview **Total Products under this Category:** 12 ## Trust & Credibility Stats **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 500+ Authentic Reviews - 12+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## Best AI Agent Observability Software At A Glance - **Best Free Software:** [Arize AI](https://www.g2.com/products/arize-ai/reviews) ## Top-Rated Products (Ranked by G2 Score) ### 1. [Arize AI](https://www.g2.com/products/arize-ai/reviews) Arize AI offers an all-in-one AI and Agent Engineering platform designed for the complexity and unpredictable behavior of generative models. With purpose-built tools to observe, evaluate, and optimize performance, teams can detect issues early, understand why they occur, and improve reliability from development through production. Open and interoperable by design, Arize enables faster iteration, safer deployments, and more reliable customer experiences while remaining agnostic to vendor, framework, and language. Prompt IDE: Design, test, and evolve prompts with live inputs, outputs, and evaluation results Tracing & Observability: Visualize every step of an agent’s behavior with Arize’s OpenInference instrumentation Evaluation: Run online and offline LLM-as-a-Judge and human feedback loops to measure accuracy and task success Continuous Improvement: Use trace analysis, evaluation feedback, and curated datasets to run experiments and improve agents Co-pilot assistant (Alyx): Ask natural language question about agent performance within the Arize platform Real-time Monitoring & Alerts: Track custom metrics, monitor latency, token usage, failures, and set alerts to stay ahead of production issues **Average Rating:** 4.2/5.0 **Total Reviews:** 28 **Seller Details:** - **Seller:** [Arize AI](https://www.g2.com/sellers/arize-ai) - **HQ Location:** Berkeley, US - **Twitter:** @arizeai (4,399 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/arizeai/about (160 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Information Technology and Services - **Company Size:** 43% Small-Business, 29% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (2 reviews) - Easy Integrations (2 reviews) - Features (2 reviews) - Capabilities (1 reviews) - Machine Learning (1 reviews) **Cons:** - Missing Features (2 reviews) - API Issues (1 reviews) - Difficult Learning (1 reviews) - Lack of Guidance (1 reviews) - Learning Curve (1 reviews) ### 2. [Monte Carlo](https://www.g2.com/products/monte-carlo/reviews) Monte Carlo is the data and AI observability leader trusted by Nasdaq, Honeywell, Roche, and hundreds of enterprise organizations worldwide. We help enterprise teams build and ship AI products with confidence — because none of it works if the data and AI underneath can't be trusted. As enterprises move AI agents into production, a new class of reliability problems has emerged. Models hallucinate. Pipelines drift. Data quality issues silently corrupt outputs before anyone notices. Traditional monitoring tools weren't built for this — they watch infrastructure, not intelligence. Monte Carlo was. Our platform ensures the data feeding your AI is trustworthy and the agents themselves behave as expected, catching failures across your entire AI supply chain before they reach end users, stakeholders, or customers. At the core of Monte Carlo is a suite of AI Observability Agents — the first of their kind — designed to do more than alert you when something breaks. They investigate. They surface root cause context automatically, correlate issues across your data and AI estate, and guide your team to resolution faster than any manual process could. For data engineers, ML engineers, and AI product teams, that means less time firefighting and more time building. Monte Carlo's end-to-end platform spans every layer of the modern data and AI stack — data, systems, code, models, and AI agents — giving teams a single place to detect, diagnose, and resolve issues at scale. Thoughtfully automated workflows reduce toil. Intuitive collaboration tools keep data and AI teams aligned. And deep integrations across your existing stack mean you get full coverage without ripping anything out. The results speak for themselves: teams using Monte Carlo dramatically reduce the time to detect and resolve data and AI incidents, scale monitoring coverage without scaling headcount, and build the internal trust that turns AI investments into business outcomes. Consistently ranked #1 in data observability on G2, Monte Carlo sets the industry standard for data and AI reliability. If your organization is serious about AI — serious enough to put it in front of customers, executives, and critical decisions — Monte Carlo is the foundation it needs. **Average Rating:** 4.3/5.0 **Total Reviews:** 506 **Seller Details:** - **Seller:** [Monte Carlo](https://www.g2.com/sellers/monte-carlo) - **Company Website:** https://www.montecarlodata.com/ - **HQ Location:** San Francisco, US - **Twitter:** @montecarlodata (1,576 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/monte-carlo-data/ (576 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Engineer, Senior Data Engineer - **Top Industries:** Financial Services, Computer Software - **Company Size:** 49% Enterprise, 43% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (112 reviews) - Alerts (107 reviews) - Monitoring (97 reviews) - Alerting System (78 reviews) - Data Quality (53 reviews) **Cons:** - Alert Management (68 reviews) - Alert Overload (62 reviews) - Inefficient Alert System (53 reviews) - UX Improvement (49 reviews) - Limited Functionality (44 reviews) ### 3. [Fiddler AI](https://www.g2.com/products/fiddler-ai/reviews) Fiddler is a pioneer in Model Performance Management for responsible AI. The Fiddler platform’s unified environment provides a common language, centralized controls, and actionable insights to operationalize ML/AI with trust. Model monitoring, explainable AI, analytics, and fairness capabilities address the unique challenges of building in-house stable and secure MLOps systems at scale. Unlike observability solutions, Fiddler integrates deep XAI and analytics to help you grow into advanced capabilities over time and build a framework for responsible AI practices. Fortune 500 organizations use Fiddler across training and production models to accelerate AI time-to-value and scale, build trusted AI solutions, and increase revenue . For more information, visit www.fiddler.ai or follow us on Twitter @fiddlerlabs. Sign up for a 14-day free trial: www.fiddler.ai/trial **Average Rating:** 4.3/5.0 **Total Reviews:** 3 **Seller Details:** - **Seller:** [Fiddler](https://www.g2.com/sellers/fiddler) - **Year Founded:** 2018 - **HQ Location:** Palo Alto, US - **LinkedIn® Page:** http://linkedin.com/company/fiddler-ai (103 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 100% Small-Business ### 4. [Maxim AI](https://www.g2.com/products/maxim-ai/reviews) At Maxim, we are building an end-to-end evaluation stack to help development teams evaluate AI applications and iteratively improve them. Our platform streamlines the entire lifecycle of AI applications, right from prompt engineering (experimentation, versioning, deployment) to pre-release testing for quality and functionality, data-set creation and management for testing and fine-tuning, and post-release monitoring. Our goal is to help development teams ship high quality AI products, faster. **Average Rating:** 4.8/5.0 **Total Reviews:** 3 **Seller Details:** - **Seller:** [Maxim AI](https://www.g2.com/sellers/maxim-ai) - **Year Founded:** 2023 - **HQ Location:** San Francisco, US - **Twitter:** @getMaximAI (373 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/maxim-ai/ (11 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 33% Enterprise, 33% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (3 reviews) - Easy Integrations (2 reviews) - Alerting System (1 reviews) - Annotation Efficiency (1 reviews) - Automation (1 reviews) **Cons:** - Poor Documentation (1 reviews) ### 5. [Superwise](https://www.g2.com/products/superwise-ai-superwise/reviews) As more businesses rely on AI models to boost their impact and their bottom-line, the need for managing, monitoring and optimizing the real-life behaviour of these models grows. Superwise.ai is the company that monitors and assures the health of AI models in production. Already used by top-tier organizations, Superwise.ai monitors millions of predictions daily to eliminate the risks derived by these models’ black-box nature: bad decisions, unwanted bias, and compliance issues. Their AI assurance solution acts as the one source of truth for all the stakeholders, and empowers data science and operational teams with the right insights to scale their use of AI by becoming more independent, agile, and gain confidence in their models’ operations. Implemented use cases include Customer Lifetime Value (CLV) predictions, fraud detection, lead scoring, underwriting, credit risk, and more. Recognized for its innovative technology and approach, Gartner recently named superwise as a 2020 Cool Vendor in Enterprise AI Governance. **Average Rating:** 4.0/5.0 **Total Reviews:** 2 **Seller Details:** - **Seller:** [superwise.ai](https://www.g2.com/sellers/superwise-ai) - **Year Founded:** 2017 - **HQ Location:** Nashville, US - **LinkedIn® Page:** https://www.linkedin.com/company/superwise-ai (95 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 100% Small-Business ### 6. [AgentOps](https://www.g2.com/products/agentops/reviews) AgentOps is a comprehensive developer platform designed to enhance the reliability and performance of AI agents and large language model (LLM) applications. By providing advanced observability tools, AgentOps enables developers to trace, debug, and deploy AI agents with confidence. The platform supports a wide range of LLMs and frameworks, including OpenAI, CrewAI, and Autogen, facilitating seamless integration into existing workflows. With features like visual event tracking, time-travel debugging, and detailed cost monitoring, AgentOps empowers engineers to build robust and efficient AI solutions. Key Features and Functionality: - Visual Event Tracking: Monitor LLM calls, tool usage, and multi-agent interactions through an intuitive visual interface. - Time-Travel Debugging: Rewind and replay agent runs with point-in-time precision to identify and resolve issues effectively. - Comprehensive Debugging and Auditing: Maintain a complete data trail of logs, errors, and potential prompt injection attacks from prototype to production stages. - Cost Monitoring: Track token usage and manage agent expenditures with up-to-date price monitoring across multiple agents. - Extensive Integrations: Seamlessly integrate with over 400 LLMs and frameworks, including native support for top agent frameworks. Primary Value and Problem Solved: AgentOps addresses the critical need for enhanced observability and reliability in AI agent development. By offering tools that provide deep insights into agent behavior, performance metrics, and cost analysis, it enables developers to identify and rectify issues promptly. This leads to more dependable AI applications, reduced development time, and optimized resource utilization, ultimately accelerating the deployment of production-grade AI solutions. **Seller Details:** - **Seller:** [AgentOps](https://www.g2.com/sellers/agentops) - **Year Founded:** 2023 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/aistaff (528 employees on LinkedIn®) ### 7. [Arize Phoenix](https://www.g2.com/products/arize-phoenix/reviews) Phoenix helps you understand and improve AI applications by giving you a workflow for debugging and iteration. You can send detailed logging information, known as traces, from your app to see exactly what happened during a run, score outputs using evaluation tests to identify failures and regressions, iterate on your prompts using real production examples, and optimize your app with experiments that compare changes on the same inputs. Together, these tools help you move from inspecting individual runs to improving quality with evidence. **Seller Details:** - **Seller:** [Arize AI](https://www.g2.com/sellers/arize-ai) - **HQ Location:** Berkeley, US - **Twitter:** @arizeai (4,399 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/arizeai/about (160 employees on LinkedIn®) ### 8. [Braintrust](https://www.g2.com/products/braintrust-2024-12-22/reviews) Braintrust empowers teams to build production-grade AI apps with confidence. Our platform seamlessly integrates code and prompt development with a UI for evaluating models, searching logs, and testing ideas. By bridging your development environment and Braintrust, we enable faster iteration, automatic optimization, and better collaboration—unlocking the full potential of LLMs for every product. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **Seller Details:** - **Seller:** [Braintrust](https://www.g2.com/sellers/braintrust-70da938f-eb27-4a47-ab01-a0bb5c7c9102) - **Year Founded:** 2023 - **HQ Location:** San Francisco, California, United States - **LinkedIn® Page:** https://www.linkedin.com/company/braintrust-data (53 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 100% Small-Business ### 9. [Honeyhive AI](https://www.g2.com/products/honeyhive-ai/reviews) HoneyHive is a comprehensive AI observability and evaluation platform designed to assist developers and domain experts in building reliable AI applications efficiently. It offers tools for testing, debugging, monitoring, and optimizing AI agents, catering to both startups and large enterprises. HoneyHive addresses the challenges of deploying reliable AI agents by providing a unified platform that integrates testing, debugging, monitoring, and optimization tools. It enables teams to systematically measure AI quality, gain comprehensive visibility into agent interactions, and continuously monitor performance metrics. By bridging the gap between development and production environments, HoneyHive ensures that AI applications are robust, efficient, and scalable, thereby instilling confidence in their deployment and operation. **Seller Details:** - **Seller:** [HoneyHive](https://www.g2.com/sellers/honeyhive) - **Year Founded:** 2022 - **HQ Location:** New York, US - **LinkedIn® Page:** https://www.linkedin.com/company/honeyhive-ai (11 employees on LinkedIn®) ### 10. [Langfuse](https://www.g2.com/products/langfuse/reviews) Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications. At its core, Langfuse provides traces (observability), evals, prompt management and metrics to understand the performance and quality of LLM applications. Langfuse takes security seriously. Langfuse can be self-hosted in your own VPC or on-prem. Langfuse also offers a managed cloud version that is SOC2 Type2 and ISO27001 certified as well as GDPR compliant. **Seller Details:** - **Seller:** [Langfuse](https://www.g2.com/sellers/langfuse) - **Year Founded:** 2022 - **HQ Location:** Berlin, Germany - **Twitter:** @langfuse (4,647 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/langfuse/ (3 employees on LinkedIn®) ### 11. [LangSmith](https://www.g2.com/products/langsmith/reviews) LangSmith Observability gives you complete visibility into agent behavior. ‍ Trace your preferred framework or integrate LangSmith with any agent stack using our Python, Typescript, Go, or Java SDKs. **Seller Details:** - **Seller:** [Langchain](https://www.g2.com/sellers/langchain) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/langchain/ (188 employees on LinkedIn®) ### 12. [Zenity](https://www.g2.com/products/zenity/reviews) Founded in 2021, Zenity brings application security controls to the world of business-led development and AI adoption. The Zenity platform is built from the ground up with a security-first approach centered on three pillars: Visibility, Risk Assessment, and Governance. As the founding member of the OWASP Top 10 project specifically focused on low-code/no-code development, Zenity takes a community-oriented approach to this rapidly evolving security vector. With SOC 2 Type 2 and GDPR compliance, Zenity’s agent-less platform is uniquely positioned to help enterprises truly know their business apps, and helps organizations with identifying how copilots, AI, and low-code/no-code platforms are being used, the business context for each individual app developed on those platforms, and providing governance to ensure secure development. For more information, visit us at https://www.zenity.io **Seller Details:** - **Seller:** [Zenity](https://www.g2.com/sellers/zenity) - **Year Founded:** 2021 - **HQ Location:** Tel-Aviv, IL - **LinkedIn® Page:** https://www.linkedin.com/company/zenitysec/ (124 employees on LinkedIn®) ## Parent Category [Monitoring Software](https://www.g2.com/categories/monitoring)