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Don't Trust Your AI Agents. Test Them Like a Scientist — Stephanie Jarmak, Omni Analytics

Published

23 October 2026

Topic

opportunities

◆ Sectors

AI AgentsMLOps

◆ Geography

United States

◆ Source

Read at finance.biggo.com →

◆ Verified

Fusion42 · 24 October 2026 · Fusion42 review

Stephanie Jarmak of Omni Analytics explains that AI agent evaluations must be conducted within the specific context and workloads of the deploying environment, warning that standard public benchmarks can mislead by shifting model rankings significantly. She advocates for building internal, real-workload benchmarks and detailed debugging methods to ensure AI agents perform reliably in production.

This Wire brief sits within Fusion42's coverage of AI Agents and MLOps.

◆ ◆ The Wire takeaway

You must calibrate AI agent tests to your real workflows to avoid false confidence. Running your own benchmarks and fully tracing agent use will protect you from costly deployment failures.

◆ Coverage

1 source · 23 Oct 2026

◆ Related on Wire

◆ Topics

AI AgentsMLOpsai-agentsevaluation-methodsbenchmarkingproduction-testingtrust-and-safety