Best AI Agent Observability Software - Page 2

Acceldata

Acceldata is the Data and AI platform for Autonomous Enterprise. Large enterprises need uniform data access, compute capabilities and governance to run analytics and deploy agents across their hybrid infrastructure. But most enterprise data platforms were built on the assumption that data would eventually centralize into a single warehouse or lakehouse. In practice, large enterprises operate four or more data platforms simultaneously, with data distributed across cloud providers, on-premises systems, and regulated environments that cannot move data across borders. Acceldata addresses this by bringing compute to wherever data already resides, rather than forcing data to move to the compute engine. Acceldata supports every stage of the data lifecycle, on any platform, bringing compute to where the data lives and ensuring data trust by unifying data quality, governance and observability into an intelligent, AI-driven fabric. Trusted by Fortune 500 companies worldwide, Acceldata empowers businesses to unlock the full potential of their data in the AI era. Key capabilities include: 1. Data and AI Observability: End-to-end monitoring of data pipelines, data quality, AI agent behavior, and LLM outputs. Includes anomaly detection, data lineage, reconciliation, and alert management across environments. 2. Agentic Data Management (ADM): Deploys autonomous agents to automate data quality monitoring, pipeline operations, catalog management, and incident response across distributed data estates. 3. Agentic Data Engineering (ADE): Builds, orchestrates, and runs data pipelines using intelligent agents that automate pipeline creation, federated querying, and job management. 4. Data Warehousing: Executes queries in-place across lakehouses and warehouses using a Velox-accelerated engine, supporting open formats including Apache Iceberg, Delta Lake, Hudi, and Parquet with no vendor lock-in. 5. Data Platform Modernization: Provides phased migration paths for enterprises running Hadoop or Cloudera infrastructure, with in-place, sidecar, and forklift migration options built on an open-source foundation. Acceldata is designed for large enterprises in financial services, life sciences, telecommunications, manufacturing, retail, and insurance, particularly organizations operating regulated data environments where governance and data residency requirements prevent full cloud migration. The platform integrates with Snowflake, Databricks, AWS, Azure, GCP, and major open-source data frameworks.

Average Rating: 4.4/5.0

Total Reviews: 54

AgentOps

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.

AgentPulse

Agnost AI

AI Agents Control Tower

AI Agent Testing

AI Agent Testing platform for testing AI agents, security, accuracy, reliability, hallucinations, and production readiness.

Aide

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 support and business operations — bringing intelligence to the support stack. Our agentic AI platform helps exceptional customer experience teamsdeliver faster resolution, improved customer satisfaction, measurable cost reduction, and intelligence that compounds, making AI your competitive advantage. Companies that use Aide continually exceed their customers' expectations, ultimately increasing loyalty, retention and lifetime value. Trusted by financial services teams, technical product teams, e-commerce brands, and education providers to reduce resolution time and scale support operations intelligently, while maintaining the quality of customer service. Aide is built to deliver real outcomes for companies with serious operational pain and volume where speed to value and interaction quality matter most. It is designed for ease of adoption and confident deployment to help teams deliver exceptional customer experiences.

ashr

Ashr is a comprehensive platform designed to assist teams in building, evaluating, and monitoring AI agents, ensuring they function correctly before deployment. It offers tools for offline evaluation, production observability, and real-time analytics, enabling developers to identify and rectify issues proactively. Key Features and Functionality: - Testing Platform: Allows for the generation of datasets, running agents against test scenarios offline, and comparing expected versus actual behavior. - Observability: Provides tracing of agents' production behavior, including LLM calls, tool invocations, retrieval steps, latency, and errors. - Python SDK: Offers a lightweight SDK with zero external dependencies, facilitating seamless integration into existing codebases. - Voice Session Analysis: Supports real-time voice agents on LiveKit, with features like turn timelines, transcripts, per-stage cost and latency analysis, mixed-audio replay, and barge-in metrics. Primary Value and Problem Solved: Ashr addresses the challenges of ensuring AI agents operate correctly by providing a robust framework for testing and monitoring. By identifying and fixing failures before they reach end-users, Ashr enhances the reliability and performance of AI systems, reducing downtime and improving user satisfaction.

ChatSee.ai

Dataiku Agent Management

Dataiku Agent Management is a standalone product designed to provide enterprises with comprehensive oversight and control over their AI agents, regardless of the platforms on which they were developed. As organizations rapidly adopt AI agents across various departments, maintaining visibility and governance becomes increasingly challenging. Dataiku Agent Management addresses this by offering a centralized system to discover, monitor, and manage AI agents, ensuring they deliver value while mitigating associated risks. One of the primary features of Dataiku Agent Management is its ability to scan and inventory AI agents across multiple platforms, including AWS Bedrock, Databricks Agents, Google Vertex, Microsoft Copilot Studio, Azure Foundry, Salesforce Agentforce, Snowflake Cortex, and Dataiku itself. This cross-platform compatibility ensures that enterprises have a unified view of all their AI agents, regardless of their origin. Each agent is cataloged with essential details such as ownership, purpose, and operational status, providing a clear understanding of the organization's AI landscape. Monitoring is another critical component of Dataiku Agent Management. The system tracks each agent's usage, cost, and performance metrics, allowing organizations to assess whether agents are functioning as intended and delivering the expected business value. By identifying deviations or performance issues early, enterprises can proactively address potential problems before they escalate. Risk management is integral to the platform. Dataiku Agent Management enables organizations to certify agents, conduct scheduled tests, and maintain comprehensive records of risk assessments and mitigations. This structured approach ensures that high-risk agents, especially those handling sensitive data or critical operations, are continuously evaluated and compliant with regulatory standards. The platform's ability to maintain an audit-ready record simplifies the process of responding to inquiries from managers, auditors, or regulators. Unlike traditional agent monitoring tools that are often confined to specific vendor ecosystems, Dataiku Agent Management is deliberately agnostic. It operates above the individual platforms, providing a holistic view of the entire agent portfolio. This overarching perspective allows organizations to identify unmonitored agents, assess risk concentrations, and evaluate the return on investment for each agent. Users can interact with the system using plain language queries, receiving comprehensive insights that span the entire AI agent landscape. In summary, Dataiku Agent Management equips enterprises with the tools necessary to effectively oversee their AI agents. By offering discovery, monitoring, and risk management capabilities across diverse platforms, it ensures that AI agents are aligned with business objectives, operate efficiently, and adhere to governance standards. This centralized approach empowers organizations to harness the full potential of their AI initiatives while maintaining control and compliance.

Glass

Honeyhive AI

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.

Iris Eval

LatentEval

LumiqTrace AI

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, sub-agent handoff, and planning span as a structured trace. Each span carries agent identity you can see which agent owns each step, what context was passed during delegation, what came back, and the latency and cost of each sub-execution. An agent map is built automatically from live execution data without manual configuration. Setup uses provider auto-patching: initializing LumiqTrace silently instruments OpenAI, Anthropic, Gemini, Bedrock, and Mistral calls. Framework-level tracing adds one handler per framework LangChain, CrewAI, Google ADK, and OpenAI Agents SDK are supported. The platform includes 12 built-in LLM-as-judge evaluation templates faithfulness, relevance, toxicity, groundedness, and others that run automatically on every trace. No custom scoring functions required. A cost optimizer analyzes trace data to surface token waste, inefficient prompt patterns, and model swap opportunities with estimated dollar savings. LumiqPilot is an AI ops assistant within the platform. It reads live trace data to answer questions about cost spikes, failure patterns, or latency regressions. From the same interface, users can create alerts, switch models, or roll back prompts. Free tier includes 10,000 traces per month with no credit card required.