Full-Fidelity Agent Tracing

Arize AX provides end-to-end tracing for every agent interaction, capturing the full trajectory of actions, thoughts, and tool calls. This gives teams a complete picture of what an agent did, not just the final output. It is built on the OpenInference standard, ensuring compatibility with the broader AI ecosystem.

Business outcome: Drastically reduces the time to identify and diagnose the root cause of agent failures in production.

Comprehensive Evaluation Framework

The platform enables teams to run span, trace, and session-level evaluations at scale. This includes online evals on live traffic and offline evals on datasets, allowing for continuous performance monitoring. It is designed to answer the critical question: is my agent getting better or worse?

Business outcome: Provides objective, data-driven metrics to track AI quality and ensure improvements are real and measurable.

Signal-Driven Issue Resolution

Arize AX includes 'Signal', a feature that automatically identifies failure modes in agents and can even propose fixes by creating pull requests. This moves the platform from passive monitoring to active improvement, suggesting concrete actions to resolve issues. This capability is a significant step toward self-improving agents.

Business outcome: Accelerates the iteration cycle by automating the detection and initial remediation of common agent issues.

Rapid Testing with Playgrounds

Teams can use the built-in playgrounds to test prompts and harnesses against production data before deploying them. This allows for safe experimentation and validation, reducing the risk of introducing regressions. It creates a fast feedback loop between development and production.

Business outcome: Enables faster, safer iteration on agent prompts and configurations, improving overall quality with less risk.

Secure & Flexible Deployment

Arize supports flexible deployment options, including SaaS and self-hosted environments, and is certified to standards like SOC 2 Type II, ISO 27001, and HIPAA. This ensures that sensitive data remains under the organization's control and meets strict compliance requirements. The platform is designed for enterprise-grade security.

Business outcome: Allows businesses in regulated industries to leverage advanced AI observability without compromising on data privacy or security.

Built on Open Standards

The platform is built on open-source tools like Phoenix and the OpenInference standard. This provides transparency and interoperability, allowing teams to integrate Arize with their existing stack and avoid proprietary lock-in. It is a key differentiator for teams that value control and flexibility.

Business outcome: Reduces long-term risk and increases flexibility by ensuring the observability platform integrates seamlessly with the broader AI ecosystem.

• Enterprise-Grade ScaleArize is built to handle massive production workloads, evidenced by its claims of processing over a trillion spans per month.
• Comprehensive EvaluationThe platform offers a more complete evaluation framework than most competitors, covering spans, traces, and sessions.
• Open Standards FoundationBeing built on OpenInference and OpenTelemetry ensures strong interoperability and reduces the risk of vendor lock-in.
• Actionable InsightsThe 'Signal' feature goes beyond simple monitoring to propose fixes, making it a proactive tool for improving agent performance.

• Complex SetupInitial instrumentation and setup can be complex and requires a dedicated AI engineering team to manage effectively.
• Pricing for ScaleWhile the free and pro tiers are accessible, the cost can increase significantly for enterprise-level usage with custom needs.
• Feature OverloadThe sheer number of features and capabilities can be overwhelming for smaller teams or those new to AI observability.
• Professional RealityArize is a powerful tool, but its full value is only realized by teams with the expertise to design and manage a robust evaluation and observability strategy.

Debugging Complex Agent Workflows

For teams building multi-step agents with tools like LangChain or CrewAI, Arize provides the necessary tracing to understand and fix failures. It offers a clear view of the entire agent trajectory, which is essential for complex systems.

Automated Regression Testing

AI teams can use Arize to run continuous evaluations on new prompts or model versions against historical datasets. This helps catch regressions early in the development cycle, ensuring quality before deployment.

Meeting Strict SLOs

For businesses where AI performance is tied to service level objectives, Arize provides the monitoring and alerting needed to ensure reliability. It allows teams to track latency, cost, and quality metrics in real-time.

Improving Agent Performance

By analyzing production data and using the 'Signal' feature, teams can identify areas for improvement and test fixes in the playground. This creates a data-driven loop for continuously enhancing agent capabilities.

Sign up for a free Arize AX account on their website.

Connect your agent or application by following the setup guide to send your first trace.

Instrument your code using the OpenInference standard to start capturing detailed span and trace data.

Use the pre-built evaluations or create custom ones to begin monitoring your agent's performance.