AgentX
AgentX delivers comprehensive evaluation capabilities for AI agents in production environments.
PaidCoding & Dev
Visit website Free to browse · updated 2026-06-22
What is AgentX?
Users can synthesize ground truth from documents and knowledge bases to maintain relevant evaluation assets over time. The system tracks agent execution across phases, identifies issues such as hallucinations through detailed timelines, and recommends targeted adjustments like prompt refinements to resolve detected problems. Evaluation covers multiple dimensions including task accuracy, tool execution reliability, reasoning coherence across interactions, and alignment with business outcomes. This layered approach helps surface patterns in agent behavior and supports iterative improvements before and after release. Continuous monitoring detects drift in prompts or data, triggering re-evaluations to keep agents aligned with expectations. By embedding these processes into release workflows, teams gain confidence in deploying and maintaining AI systems at scale.
Key features
Create test sets from unstructured data and synthesize ground truth
Multi-run and multi-step evaluation for consistency and workflows
CI/CD pipeline integration with automatic deploy gates
Continuous evaluation loop with drift monitoring
Four-layer framework: task correctness, tool reliability, reasoning, business impact
Root-cause analysis with suggested fixes and performance metrics
Prompt and dataset drift detection and alerting
What you can use AgentX for
Creating Evaluation Test Sets
Synthesize ground truth from unstructured documents or knowledge bases to build and continuously enrich test sets that remain accurate and relevant.
Multi-Run Agent Assessment
Measure consistency across repeated runs and evaluate multi-step workflows with multiple interactions while embracing non-deterministic behavior.
Agent CI/CD Pipeline Integration
Use evaluation results to automatically block failed deployments or promote passing agents, enabling confident updates through a continuous evaluation loop.
How to use AgentX
- 1Build test set from unstructured data or knowledge bases
- 2Run evaluation across multiple runs and steps
- 3Score results and surface failures with analysis
- 4Apply threshold decision for deploy or iterate
- 5Monitor production for drift and trigger re-evaluation
AgentX pricing
Pricing model: Paid. Plan details are indicative — check the site for current prices.
Enterprise
Custom
- AI observability and traceability
- Multi-run & multi-step evaluation
- CI/CD pipeline integration
- Continuous evaluation loop
- Four-layer LLM evaluation framework
Editor's verdict
Pros
- +Production-ready continuous LLM and agent evaluation
- +Handles non-deterministic behavior with reliable metrics
- +Actionable insights tied to business KPIs
Cons
- –Requires initial definition of metrics and test sets
- –Focused on enterprise production deployments
Our take: AgentX is a solid coding & dev choice. It's valued for production-ready continuous llm and agent evaluation and handles non-deterministic behavior with reliable metrics. The main trade-off is requires initial definition of metrics and test sets. Best when you need reliable, professional output.
Frequently asked questions
AI agent evaluation measures how well AI agents or LLMs perform in production beyond demos, covering task correctness, tool reliability, reasoning quality, and business impact metrics such as completion rate and user satisfaction.
Summary
AgentX is a solid coding & dev choice. It's valued for production-ready continuous llm and agent evaluation and handles non-deterministic behavior with reliable metrics. The main trade-off is requires initial definition of metrics and test sets. Best when you need reliable, professional output.
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