Overview
AgentX is a production-ready LLM evaluation framework that provides AI observability and traceability to assess AI agents and LLMs before they fail. It serves as a reliability guardrail, enabling developers to evaluate agents through four distinct layers of testing. The platform focuses on catching failures early by analyzing agent behavior, pinpointing issues, and prescribing fixes. It integrates evaluation into a CI/CD pipeline, automatically blocking or promoting deployments based on test results.
Application scenarios
Agent reliability testing
Evaluate AI agents for task correctness, tool reliability, reasoning consistency, and business impact before deployment.
CI/CD for AI agents
Build automated pipelines that block deployments on eval failures and promote to production on passes.
Continuous monitoring
Run evaluations both before deploy and continuously after, with drift detection to catch performance degradation over time.
Multi-step workflow assessment
Measure consistency across repeated runs and assess complex multi-step interactions with multiple agent calls.
Failure analysis and debugging
Analyze execution timelines, surface hidden patterns, and receive suggested fixes for detected failures like hallucinations.
A/B testing and iteration
Use evaluation results to iterate on agents, compare runs, and make data-driven decisions about updates.
Core features
Four-layer evaluation framework
Assess task correctness, tool and API reliability, reasoning and consistency, and business/user impact in a structured hierarchy.
CI/CD pipeline integration
Automatically block deployments if evals fail or promote to production if they pass, enabling confident agent updates.
Continuous evaluation loop
Run evaluations before deploy and continuously after, with automatic looping back to re-evaluate on threshold breaches.
Drift detection
Monitor agents post-deployment and trigger re-evaluation when performance drifts beyond set thresholds.
Failure analysis with suggested fixes
Analyze agent behavior to pinpoint issues, surface hidden patterns, and prescribe concrete fixes (e.g., restricting assumptions in system prompts).
Execution timeline visualization
View detailed step-by-step timelines of agent runs, including phases like initialization, preprocessing, knowledge retrieval, and ReAct loops.
Multi-run and multi-step measurement
Measure consistency across repeated runs and assess multi-step workflows with multiple interactions, embracing non-deterministic nature.
Test set creation from unstructured data
Create test sets from documents or knowledge bases and synthesize ground truth to keep evaluations accurate and relevant.
Target users
The platform is designed for developers and engineering teams building AI agents or LLM-powered applications who need robust evaluation and observability. It suits teams implementing CI/CD for AI agents, AI reliability engineers, and product teams focused on ensuring agent performance in production environments.
How to use
Start by requesting a demo through the official website. Once onboarded, users can create test sets from unstructured data, run evaluations across the four layers, and set up CI/CD pipelines that automatically block or promote deployments based on eval results. The platform provides a continuous evaluation loop for monitoring drift and re-running evaluations on threshold breaches.
Effect review
AgentX presents a comprehensive evaluation framework that goes beyond simple accuracy metrics, offering a structured approach to catching agent failures before they impact users. The inclusion of CI/CD pipeline integration and continuous monitoring makes it practical for production environments where reliability is critical. The failure analysis feature with suggested fixes is particularly valuable for developers who need actionable insights rather than just pass/fail scores. While the platform appears robust for technical teams, its effectiveness ultimately depends on how well users define their test sets and thresholds. The emphasis on multi-step reasoning and tool reliability reflects real-world agent complexity, making it a strong choice for teams serious about agent quality assurance.
Frequently asked questions
What is AgentX?
AgentX is a production-ready LLM evaluation framework that assesses AI agents and LLMs using four evaluation layers, drift detection, completion rate tracking, and A/B testing.
What are the four evaluation layers in AgentX?
The four layers evaluate different aspects of AI performance, such as correctness, safety, robustness, and efficiency, providing comprehensive insights.
How does AgentX detect drift?
AgentX monitors model outputs over time to identify shifts in performance or behavior, alerting teams to potential degradation or changes in data distribution.
Can AgentX track completion rates?
Yes, AgentX tracks completion rates to measure how often AI agents successfully finish tasks, helping identify failure patterns and improve reliability.
Does AgentX support A/B testing?
Yes, AgentX supports A/B testing, allowing you to compare different models or configurations side-by-side to determine the best performer.
Is AgentX suitable for production environments?
Yes, AgentX is designed for production use, offering scalable evaluation, real-time monitoring, and integration with existing workflows.
Launch URL
https://www.agentx.so/mcp/ai-evaluationTags
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