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The article addresses a critical shift in software engineering where agentic workloads invalidate traditional testing assumptions regarding execution time, retry costs, and reproducibility. This transition from deterministic scripts to non-deterministic agents requires new infrastructure practices to manage budget overruns and opaque failure modes that standard observability tools cannot currently track. Consequently, the piece serves as a vital update on the practical challenges of deploying autonomous AI systems in enterprise environments.
Agentic workloads break assumptions about software testing. Here’s how to cope
Most traditional enterprise systems were built around three assumptions: Jobs finish quickly, retrying one is free and the same input always produces the same output. Agentic workloads break all three of these expectations, which is why pilots that perform well turn into operational problems once they run unattended. The difficulty is rarely the model; it’s […] The post Agentic workloads break assumptions about software testing. Here’s how to cope appeared first on SiliconANGLE.
Most traditional enterprise systems were built around three assumptions: Jobs finish quickly, retrying one is free and the same input always produces the same output. Agentic workloads break all three of these expectations, which is why pilots that perform well turn into operational problems once they run unattended. The difficulty is rarely the model; it’s the surrounding infrastructure and management practices that assume properties these workloads no longer have. Agents work by different rules than conventional software. Here are three significant differences: Work takes minutes, not milliseconds. An agentic task can run long enough to exceed timeout thresholds nothing in your stack has encountered before. Systems that seemed stable start failing in ways that look mysterious until someone checks the duration. Retries now cost money. Traditionally, retry logic has been nearly…
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