What is AgentX?
AgentX is an AI observability and evaluation platform that helps developers test, monitor, and improve AI agents before they reach production. Think of it as CI/CD for AI agents—it provides full traceability, identifies failures, and even suggests fixes automatically. By simulating agent behavior across multiple LLM providers, AgentX lets you compare performance, cost, and latency to make informed deployment decisions.
Who it's for
- AI/ML engineers who need to evaluate agent reliability across different LLM providers and catch failures before deployment.
- Product teams building AI-powered features who want to tie agent performance to business KPIs like user satisfaction and completion rate.
- DevOps and MLOps practitioners looking to integrate agent evaluation into their existing CI/CD pipelines with automated pass/fail gates.
Key features
Multi-run and multi-step evaluation
AgentX measures consistency by running agents multiple times and assessing multi-step workflows with multiple interactions. It embraces the non-deterministic nature of AI agents while still providing reliable, repeatable metrics you can trust.
Continuous evaluation loop
The platform runs evaluations before deployment and continuously after. You build test sets, run evaluations, score failures, make threshold decisions, and either iterate or deploy—then monitor for drift in production.
AI-powered failure analysis and fixes
AgentX doesn't just surface problems—it analyzes agent behavior to pinpoint issues, surface hidden patterns, and prescribe fixes. For example, it can detect hallucinations causing baseless assumptions and suggest restricting system prompts or adding few-shot examples.
Four-layer evaluation framework
The platform evaluates agents across task correctness, tool and API reliability, reasoning and consistency, and business and user impact. This gives you a production-ready LLM evaluation framework that goes far beyond simple accuracy metrics.
What stands out
"Like an AI doctor for your agents—it not only identifies problems but suggests fixes."
This is the key differentiator. Most evaluation tools stop at flagging failures, but AgentX goes a step further by analyzing the root cause and recommending specific changes. Combined with its ability to create test sets from unstructured data and run evaluations across multiple LLM providers, it turns agent testing from a manual headache into an automated, actionable process.
Worth checking out if…
You're building AI agents that need to be reliable in production and want to move beyond basic accuracy metrics. AgentX is especially valuable if you're managing multi-step agent workflows, need to compare LLM providers, or want to integrate evaluation directly into your deployment pipeline with automated pass/fail gates.
