Agentic AI

Agent Evaluation

Agent evaluation encompasses the systematic assessment of AI agents regarding accuracy, reliability, cost, and business impact. It goes beyond simple model benchmarks and measures the agent's end-to-end performance in real business contexts — including tool usage, planning quality, and decision correctness.

Why does this matter?

Without systematic evaluation, companies operate blind: they don't know if their AI agent actually delivers better results than the manual process. Agent evaluation quantifies added value in business metrics — time savings, error rate, cost per transaction — and provides the basis for informed investment decisions.

How IJONIS uses this

We establish three-tier evaluation pipelines: (1) automated unit tests for individual tool calls, (2) scenario tests with LangSmith for end-to-end workflows, (3) A/B tests in production with real business metrics. Dashboards show performance trends in real time and alert on quality degradation.

Frequently Asked Questions

Which metrics should I use for agent evaluation?

Combine technical and business metrics: task completion rate, accuracy (vs. human baseline), average processing time, token cost per transaction, escalation rate, and user satisfaction. Specific metrics depend on your use case.

How often should I evaluate my AI agent?

Continuously. Automated tests run on every deployment, scenario tests weekly, and comprehensive business reviews monthly. Model updates from LLM providers can change performance without notice — continuous monitoring is therefore essential.

← Back to Glossary

Want to learn more?

Find out how we apply this technology for your business.

Start AI Readiness Check