Cortex
Evaluate, improve, and monitor AI agents with expert human judgment, derived from real workflows
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The reliability layer for agentic AI
As AI agents move into real workflows, teams need a way to evaluate performance, understand failures, improve behavior, and monitor reliability after launch.
Today, most teams are still missing the basics:
Clear ways to measure agent performance
Visibility into where and why agents fail
Continuous monitoring once agents are live
Evaluation criteria based on real workflows
A scalable way to translate company knowledge into agent behavior
Human expert review beyond automated evals and LLM-as-judge
How it works
Cortex turns agent reliability into a measurable process by evaluating real workflows, diagnosing failures, creating targeted training data, and monitoring performance.
1
Evaluation design
Define success criteria, rubrics, and scoring for the agent's task
2
Failure diagnosis
Domain experts review agent outputs and identify where, why, when, and how the agent fails
3
Expert training data
Targeted training data is produced by domain experts to address the highest-priority failure modes
4
Monitor reliability
Re-evaluate and track performance as models, prompts, workflows, and rules change
Impact
Cortex turns agent reliability into measurable business outcomes
Faster time to production
Identify failure modes earlier, improve systems faster, and reduce iteration cycles
Clear AI performance visibility
Have constant access to see why agents fail, and a solution to overcome these errors
Greater customer trust
Improve consistency and accuracy in critical AI experiences
Continuous AI improvement
Monitor performance as the agent and its operating environment evolve
