# 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 ## How Many AI Agent Observability Software Products Does G2 Track? **Total Products under this Category:** 16 ### Category Stats (May 2026) - **Average Rating**: 4.37/5 (↑0.04 vs Apr 2026) - **New Reviews This Quarter**: 9 - **Buyer Segments**: Mid-Market 50% │ Enterprise 40% │ Small-Business 10% - **Top Trending Product**: Fiddler AI (+0.083) *Last updated: May 19, 2026* ## How Does G2 Rank AI Agent Observability Software Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 500+ Authentic Reviews - 16+ 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. ## Which AI Agent Observability Software Is Best for Your Use Case? - **Best Free Software:** [Arize AI](https://www.g2.com/products/arize-ai/reviews) ## What Are the Top-Rated AI Agent Observability Software Products in 2026? ### 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 **Who Is the Company Behind Arize AI?** - **Seller:** [Arize AI](https://www.g2.com/sellers/arize-ai) - **HQ Location:** Berkeley, US - **Twitter:** @arizeai (4,461 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/arizeai/about (160 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services - **Company Size:** 43% Small-Business, 29% Mid-Market #### What Are Arize AI's Pros and 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 agent trust platform, trusted by Nasdaq, Honeywell, Roche, 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 hundreds 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. Critically, 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. **Average Rating:** 4.3/5.0 **Total Reviews:** 513 **Who Is the Company Behind Monte Carlo?** - **Seller:** [Monte Carlo](https://www.g2.com/sellers/monte-carlo) - **Company Website:** https://www.montecarlodata.com/ - **Year Founded:** 2019 - **HQ Location:** San Francisco, US - **Twitter:** @montecarlodata (1,574 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/monte-carlo-data/ (576 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Senior Data Engineer - **Top Industries:** Financial Services, Computer Software - **Company Size:** 50% Enterprise, 43% Mid-Market #### What Are Monte Carlo's Pros and 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 **Who Is the Company Behind Fiddler AI?** - **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®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 4. [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. **Average Rating:** 4.5/5.0 **Total Reviews:** 1 **Who Is the Company Behind Langfuse?** - **Seller:** [Langfuse](https://www.g2.com/sellers/langfuse) - **Year Founded:** 2022 - **HQ Location:** Berlin, Germany - **Twitter:** @langfuse (4,767 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/langfuse/ (3 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 5. [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 **Who Is the Company Behind Maxim AI?** - **Seller:** [Maxim AI](https://www.g2.com/sellers/maxim-ai) - **Year Founded:** 2023 - **HQ Location:** San Francisco, US - **Twitter:** @getMaximAI (374 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/maxim-ai/ (11 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 33% Mid-Market, 33% Small-Business #### What Are Maxim AI's Pros and 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) ### 6. [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 **Who Is the Company Behind Superwise?** - **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®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 7. [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. **Who Is the Company Behind AgentOps?** - **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®) ### 8. [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. **Who Is the Company Behind Arize Phoenix?** - **Seller:** [Arize AI](https://www.g2.com/sellers/arize-ai) - **HQ Location:** Berkeley, US - **Twitter:** @arizeai (4,461 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/arizeai/about (160 employees on LinkedIn®) ### 9. [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 **Who Is the Company Behind Braintrust?** - **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®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 10. [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. **Who Is the Company Behind Honeyhive AI?** - **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®) ### 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. **Who Is the Company Behind LangSmith?** - **Seller:** [Langchain](https://www.g2.com/sellers/langchain) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/langchain/ (291 employees on LinkedIn®) ### 12. [Netra](https://www.g2.com/products/keyvalue-software-systems-netra/reviews) 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 production. It is purpose-built for multi-step, multi-agent, and multi-tool workflows where traditional APM and LLM monitoring tools fall short. The platform is organized around four core capabilities. Observability delivers full-fidelity tracing across every LLM call, tool invocation, reasoning step, and retrieval, with real-time cost, latency, and error tracking. Evaluation enables teams to score agent quality automatically on every decision using built-in rubrics, custom LLM-as-judge evaluators, and code evaluators, with online evals running continuously on live traffic. Simulation lets teams stress-test agents against thousands of real and synthetic scenarios before production, using diverse personas and A/B comparisons against a baseline. Prompt Management provides a centralized workspace where every prompt is versioned, lineage-tracked, and rollback-safe, with every production response traceable back to its exact prompt version. Netra is built on OpenTelemetry, making it compatible with any OTLP-compliant backend and ensuring teams can get started with just 2 to 3 lines of code. It integrates with 14+ LLM providers including OpenAI, Anthropic, Google Gemini, and AWS Bedrock, and 12+ AI frameworks including LangChain, LangGraph, CrewAI, and LlamaIndex. The platform is SOC2 Type II certified and compliant with GDPR and HIPAA, with strict US and EU data residency and zero cross-region data sharing. Enterprise teams get on-premise deployment, isolated databases, and SSO. Available on a Free plan with no credit card required, a Pro plan at $39 per month, and custom Enterprise pricing. **Who Is the Company Behind Netra?** - **Seller:** [KeyValue Software Systems](https://www.g2.com/sellers/keyvalue-software-systems-36b38222-8354-45bc-9485-8258e99a8ea2) - **Year Founded:** 2015 - **HQ Location:** Kochi, IN - **LinkedIn® Page:** https://www.linkedin.com/company/keyvaluesystems (522 employees on LinkedIn®) ### 13. [NotiLens](https://www.g2.com/products/notilens/reviews) NotiLens is a smart alert platform that monitors your entire business stack and delivers real-time push notifications the instant something needs your attention. Most monitoring tools alert you when something breaks loudly. NotiLens also alerts you when something goes quietly wrong and that's the alert that saves your business. A signup flow that broke at 2am. A cron job that stopped without a trace. An AI agent that drifted off course while logs reported success. A payment flow that initiated but never completed. These silent failures cause the most damage and no other tool catches them. ML-powered anomaly detection learns what normal looks like for each event type and flags genuine outliers automatically. A cold-start calibration mode eliminates false alerts during warm-up so you only get paged when something truly deviates. Silence Alerts notify you when expected activity stops - no new order, no new signup, no payment in hours. Broken flow detection tracks multi-step event chains and fires the moment a sequence never completes. Smart Signal Alerts filter up to 97% of notification noise so only meaningful deviations reach you. The Acknowledgement system repeats critical alerts every 5 minutes until confirmed by you or a teammate. Daily AI summaries deliver a plain English digest of everything that happened across your stack. Topic-based organisation keeps every alert traceable to its source. NotiLens connects with 40+ platforms including Stripe, Shopify, GitHub, GitLab, AWS, Sentry, Datadog, Vercel, Intercom, Linear, OpenAI, Claude, Zapier, Make, and n8n. Developers get SDKs for Python, Node.js, Go, Rust, Ruby, PHP, and Java, a CLI for shell scripts and cron jobs, MCP support for AI agent monitoring, LangChain integration, and GitHub Actions support. Multi-user sharing ensures entire teams stay informed with real-time push across iOS, Android, and web. Built for founders, developers, AI builders, and small teams who need business-level visibility without enterprise complexity. **Who Is the Company Behind NotiLens?** - **Seller:** [NotiLens](https://www.g2.com/sellers/notilens) - **Year Founded:** 2026 - **HQ Location:** Mangaluru, IN - **LinkedIn® Page:** https://www.linkedin.com/company/notilens/ (1 employees on LinkedIn®) ### 14. [Respan AI](https://www.g2.com/products/respan-ai/reviews) Respan provide self-driving AI observability and evals for agents. Respan is the first proactive AI observability platform that closes the loop from evals to iteration. It automatically traces and evaluates production behavior to turn results into concrete changes teams can ship. **Who Is the Company Behind Respan AI?** - **Seller:** [Respan](https://www.g2.com/sellers/respan) - **Year Founded:** 2023 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/respan-ai/ (12 employees on LinkedIn®) ### 15. [SAP AI Agent Hub](https://www.g2.com/products/sap-ai-agent-hub/reviews) Discover, inventory, govern, and evaluate AI agents, MCP servers, and LLMs with full architecture and business context. **Who Is the Company Behind SAP AI Agent Hub?** - **Seller:** [SAP](https://www.g2.com/sellers/sap) - **Year Founded:** 1972 - **HQ Location:** Walldorf - **Twitter:** @SAP (297,228 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,341 employees on LinkedIn®) - **Ownership:** NYSE:SAP ### 16. [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 **Who Is the Company Behind Zenity?** - **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®) ## What Is AI Agent Observability Software? [Monitoring Software](https://www.g2.com/categories/monitoring)