# Best AI Agent Observability Software ## How Many AI Agent Observability Software Products Does G2 Track? **Total Products under this Category:** 23 ### Category Stats (Jul 2026) - **Average Rating:** 4.42/5 (↓0.04 vs Jun 2026) The average rating of products in this category, based on all submitted ratings - **Top Trending Product:** Arize AI (+1.38%) - Among all products in this category, Arize AI recorded the largest rating increase compared to last month _Last updated: July 27, 2026_ ## How Does G2 Rank AI Agent Observability Software Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 600+ Authentic Reviews - 23+ 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. ## G2 Grid® for AI Agent Observability Software ![G2 Grid® for AI Agent Observability Software plotting products by satisfaction and market presence](https://www.g2.com/categories/ai-agent-observability/grids.png?focus%5B%5D=1844139&focus%5B%5D=142449&focus%5B%5D=149556) Highlighted products: LangSmith, Monte Carlo, and Arize AI. Underlying data: [Grid® JSON](https://www.g2.com/categories/ai-agent-observability/grids.json?focus%5B%5D=langsmith&focus%5B%5D=monte-carlo&focus%5B%5D=arize-ai) **Sponsored** ### Monte Carlo Monte Carlo is the agent trust platform, trusted by Nasdaq, Cisco, PepsiCo, and hundreds of enterprise organizations worldwide. Founded in 2019 and backed by leading investors, Monte Carlo pioneered data observability and has expanded into the full AI reliability stack. We're consistently ranked #1 in data observability on G2 — and we're built for what comes next. As enterprises scale from dozens to thousands of AI agents across mission-critical use cases, Monte Carlo monitors, troubleshoots, and improves both those agents and the underlying data powering them. Our platform covers the full trust stack — from the data pipelines feeding agents, to the context they retrieve, the decisions they make, and the outputs they produce — across four trust dimensions: context quality, performance, behavior, and outputs. Only Monte Carlo closes the full trust loop across both data and AI, and we meet enterprises wherever they are on the spectrum from human-guided oversight to fully autonomous operations. With 100+ integrations across Snowflake, Databricks, and the rest of your stack, you get full coverage without ripping anything out. Traditional monitoring tools stop at the pipeline or cover only one dimension of reliability — leaving teams to manually investigate, diagnose, and fix failures across disconnected tools. Monte Carlo closes that gap. Teams using Monte Carlo dramatically reduce 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 real business outcomes. If your organization is serious enough about AI to put it in front of customers, executives, and critical decisions — Monte Carlo is the foundation it needs. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=1013792&secure%5Bchosen_at%5D=2026-07-28T11%3A27%3A00Z&secure%5Bdisplayable_resource_id%5D=1013792&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1013792&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=142449&secure%5Bresource_id%5D=1013792&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fai-agent-observability&secure%5Btoken%5D=0e910d10c96d9eeaa28b5bcc372397f9d4e0bcab673a7595624870229bb5fa92&secure%5Burl%5D=https%3A%2F%2Fmontecarlo.ai%2F%3Futm_source%3Dg2_clicks%26utm_medium%3Dpeer_review%26utm_term%3Dai_agent_observability&secure%5Burl_type%5D=custom_url) [ LangSmith ](https://www.g2.com/products/langsmith/reviews) By [Langchain](https://www.g2.com/sellers/langchain) [ 4.4/5(33) ](https://www.g2.com/products/langsmith/reviews) What do users say? Users consistently praise the tracing and debugging capabilities of LangSmith, which provide clear visibility into LLM workflows and help identify issues quickly. The intuitive UI and ease of setup ma [ Monte Carlo ](https://www.g2.com/products/monte-carlo/reviews) By [Monte Carlo](https://www.g2.com/sellers/monte-carlo) [ 4.3/5(534) ](https://www.g2.com/products/monte-carlo/reviews) What do users say? Users consistently praise the ease of use and automated monitoring features of Monte Carlo, which streamline data observability and alerting processes. The platform's ability to quickly surface anomal Pros and Cons [ Ease of Use (104) ](https://www.g2.com/products/monte-carlo/reviews?qs=pros-and-cons)[ Alert Management (58) ](https://www.g2.com/products/monte-carlo/reviews?qs=pros-and-cons) [ Arize AI ](https://www.g2.com/products/arize-ai/reviews) By [Arize AI](https://www.g2.com/sellers/arize-ai) [ 4.3/5(36) ](https://www.g2.com/products/arize-ai/reviews) What do users say? Users consistently praise the product for its intuitive navigation and responsive support, which facilitate effective monitoring of machine learning models. The platform's ability to provide actionabl Pros and Cons [ Ease of Use (4) ](https://www.g2.com/products/arize-ai/reviews?qs=pros-and-cons)[ Missing Features (3) ](https://www.g2.com/products/arize-ai/reviews?qs=pros-and-cons) [ Braintrust ](https://www.g2.com/products/braintrust-2024-12-22/reviews) By [Braintrust](https://www.g2.com/sellers/braintrust-70da938f-eb27-4a47-ab01-a0bb5c7c9102) [ 4.1/5(13) ](https://www.g2.com/products/braintrust-2024-12-22/reviews) What do users say? Users consistently praise the platform for its powerful AI evaluation and efficient testing capabilities, which streamline the process of developing and improving AI applications. The clean interface [ Arize Phoenix ](https://www.g2.com/products/arize-phoenix/reviews) By [Arize AI](https://www.g2.com/sellers/arize-ai) [ 4.5/5(5) ](https://www.g2.com/products/arize-phoenix/reviews) Product Description 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 [ Chronoloq ](https://www.g2.com/products/chronoloq/reviews) By [Chronoloq](https://www.g2.com/sellers/chronoloq) [ 5/5(2) ](https://www.g2.com/products/chronoloq/reviews) Product Description Chronoloq is an AI and API security platform for small and mid-sized organizations. It scans a company's AI/LLM features and API endpoints to identify exposed attack surface, then delivers a prioritiz [ Fiddler AI ](https://www.g2.com/products/fiddler-ai/reviews) By [Fiddler](https://www.g2.com/sellers/fiddler) [ 4.3/5(3) ](https://www.g2.com/products/fiddler-ai/reviews) Product Description 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 operati Pros and Cons [ Capabilities (1) ](https://www.g2.com/products/fiddler-ai/reviews?qs=pros-and-cons)[ Difficult Learning (1) ](https://www.g2.com/products/fiddler-ai/reviews?qs=pros-and-cons) [ Langfuse ](https://www.g2.com/products/langfuse/reviews) By [Langfuse](https://www.g2.com/sellers/langfuse) [ 4.5/5(1) ](https://www.g2.com/products/langfuse/reviews) Product Description 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), eval [ Maxim AI ](https://www.g2.com/products/maxim-ai/reviews) By [Maxim AI](https://www.g2.com/sellers/maxim-ai) [ 4.8/5(3) ](https://www.g2.com/products/maxim-ai/reviews) Product Description 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 applicatio Pros and Cons [ Ease of Use (3) ](https://www.g2.com/products/maxim-ai/reviews?qs=pros-and-cons)[ Poor Documentation (1) ](https://www.g2.com/products/maxim-ai/reviews?qs=pros-and-cons) [ Superwise ](https://www.g2.com/products/superwise-ai-superwise/reviews) By [superwise.ai](https://www.g2.com/sellers/superwise-ai) [ 4/5(2) ](https://www.g2.com/products/superwise-ai-superwise/reviews) Product Description 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 c Pros and Cons [ Analytics (1) ](https://www.g2.com/products/superwise-ai-superwise/reviews?qs=pros-and-cons)[ Expensive (1) ](https://www.g2.com/products/superwise-ai-superwise/reviews?qs=pros-and-cons) [ AgentOps ](https://www.g2.com/products/agentops/reviews) By [AgentOps](https://www.g2.com/sellers/agentops) [ 0/5(0) ](https://www.g2.com/products/agentops/reviews) Product Description 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, [ Aide ](https://www.g2.com/products/aide/reviews) By [Aide](https://www.g2.com/sellers/aide) [ 0/5(0) ](https://www.g2.com/products/aide/reviews) Product Description Aide consolidates support tools into a unified and reactive system that handles every step of the support workflow —identifying issues, solving them automatically, and suggesting optimizations to supp [ Honeyhive AI ](https://www.g2.com/products/honeyhive-ai/reviews) By [HoneyHive](https://www.g2.com/sellers/honeyhive) [ 0/5(0) ](https://www.g2.com/products/honeyhive-ai/reviews) Product Description 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, de [ LumiqTrace AI ](https://www.g2.com/products/lumiqtrace-ai/reviews) By [LumiqTrace](https://www.g2.com/sellers/lumiqtrace) [ 0/5(0) ](https://www.g2.com/products/lumiqtrace-ai/reviews) Product Description LumiqTrace is an AI agent observability platform that tracks what happens inside multi-agent systems during production execution. When an agent runs, LumiqTrace captures every decision, tool call, su [ Netra ](https://www.g2.com/products/keyvalue-software-systems-netra/reviews) By [KeyValue Software Systems](https://www.g2.com/sellers/keyvalue-software-systems-36b38222-8354-45bc-9485-8258e99a8ea2) [ 0/5(0) ](https://www.g2.com/products/keyvalue-software-systems-netra/reviews) Product Description Netra is an end-to-end AI observability, evaluation, and simulation platform that gives engineering teams complete visibility into every decision their AI agents make, from development through product - ‹ Prev‹ Prev - 1 - [2](/categories/ai-agent-observability?order=g2_score&page=2#product-list) - [Next ›Next ›](/categories/ai-agent-observability?order=g2_score&page=2#product-list) Spotlight Categories [Online Backup Software](https://www.g2.com/categories/online-backup) [SAP Store Software](https://www.g2.com/categories/sap-store) [Third Party & Supplier Risk Management Software](https://www.g2.com/categories/third-party-supplier-risk-management) [Account-Based Orchestration Platforms](https://www.g2.com/categories/account-based-orchestration-platforms) [Attribution Software](https://www.g2.com/categories/attribution) Similar Categories - [Application Performance Monitoring (APM)](/categories/application-performance-monitoring-apm) - [Cloud Infrastructure Monitoring](/categories/cloud-infrastructure-monitoring) - [Database Monitoring](/categories/database-monitoring) - [Enterprise Monitoring](/categories/enterprise-monitoring) - [Hardware Monitoring](/categories/hardware-monitoring) - [Log Monitoring](/categories/log-monitoring) - [Network Monitoring](/categories/network-monitoring) - [Observability Pipeline](/categories/observability-pipeline) - [Observability Software](/categories/observability-software) - [Other Monitoring](/categories/other-monitoring) - [Website Monitoring](/categories/website-monitoring) [Browse AI Agent Observability Themes](/categories/ai-agent-observability/themes) ![Tian Lin](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Tian Lin") TL Researched and written by [Tian Lin](https://research.g2.com/insights/author/tian-lin) Updated April 28, 2026 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 Show More