Problem solved

Developers and AI teams often struggle to ensure their AI agents behave reliably and efficiently. Manual testing is time-consuming and error-prone, while existing observability tools lack automated remediation. AgentX solves this by providing instant evaluation, pinpointing exactly where and why an agent fails, and automatically applying corrective measures, reducing debugging time and improving agent quality.

Use cases

• Automated regression testing of customer support chatbots after prompt changes
• Debugging multi-step agent workflows to find where reasoning breaks down
• Benchmarking an agent's performance against a golden dataset before deployment
• Monitoring production agents and alerting on unexpected behavior like high hallucination rates
• A/B testing different prompt strategies or model choices to optimize outcomes
• Identifying and fixing latency bottlenecks in agent pipelines with one click

Audience

• Developers
• ML Engineers
• Data Scientists

Pros

• One-click automated fixes reduce manual debugging effort
• Supports evaluation across diverse metrics and custom test suites
• Real-time monitoring and alerting for production agents
• Integrates with popular agent frameworks for easy adoption
• Visual debugging helps understand complex agent decisions

Cons

• Pricing and usage limits are not publicly available upfront
• Effectiveness of 'one-click fix' may depend on agent complexity and framework compatibility
• Reliance on a third-party service for critical agent infrastructure adds dependency