AI-generated editorial illustration; not a documentary photograph.
An infrastructure explainer published by The New Stack on September 18, 2026 argues that production AI agents create less predictable computing workloads than conventional chat applications. Sequential model calls, external tools and uneven GPU demand can turn an agent that performs well in testing into a slower and more expensive system under shared production load.
One Request Becomes a Workflow
A chatbot commonly handles a message with a single inference call before waiting for more user input. An agent may instead search documentation, query databases, call APIs, execute code and evaluate results, invoking the model again after each step. This loop makes overall response time dependent on the complete workflow rather than model generation speed alone.
The source illustrates the distinction with development and operations tasks such as reviewing a pull request or investigating a checkout-latency spike. In both cases, the agent coordinates several model and tool operations before returning an answer.
Delays Accumulate Across Steps
Because these operations are sequential, a slow database query, diagnostic or test run blocks subsequent reasoning. A failure anywhere in the chain can appear to the user as an agent that stalled or abandoned the task, even when the model itself remains responsive.
This changes the reliability target for teams deploying agents. They must monitor inference, orchestration and external dependencies together, while ensuring that individual workflow steps receive sufficient resources when multiple agents are active.
Tool Calls Produce Bursty GPU Demand
Agent workflows can pause while external systems respond and then resume in clusters. During a pause, accumulated context may leave GPU memory; when work resumes, the system may need to process that context again. Many simultaneous resumptions can therefore create sharp demand spikes despite modest average utilization.
The New Stack says infrastructure designed around steadier request traffic may respond with inconsistent latency, poor GPU utilization or unexpectedly high costs. Provisioning only for average demand can also leave insufficient capacity during peaks, particularly at the upper end of latency distribution.
What Teams Must Account For
The explainer recommends evaluating infrastructure across the entire dependency chain, including its ability to absorb rapid demand changes and avoid keeping excess capacity active during idle periods. It also cautions that purpose-built systems cannot eliminate the cost of large bursts or workflows that accumulate substantial context; they can only make those limits and costs more predictable.
Source: Your agent is only as good as your infrastructure
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