arXiv:2606.30560v1 Announce Type: new Abstract: Coding agents are rapidly becoming a major application of agentic LLMs, but serving them efficiently remains challenging. Progress on this challenge requires understanding real workload patterns, yet the data needed for such analysis is largely absent. Existing public traces and benchmarks do not capture real, day-to-day coding-agent usage across multiple agents and model families for serving-system analysis. To help fill this gap, we collect and release a trace of roughly 4,300 coding-agent sessions, containing about 350,000 LLM steps and 430,00
The rapid development and adoption of LLMs as agentic systems necessitate deeper understanding of their real-world usage patterns to optimize serving infrastructure.
Efficiently serving AI agents is crucial for scaling their deployment and reducing operational costs, directly impacting the economic viability and widespread adoption of this technology.
This data provides a foundational resource for developing more efficient LLM serving systems, allowing for better resource allocation and performance tuning specifically for agentic workloads.
- · AI infrastructure providers
- · Cloud computing platforms
- · Developers of agentic LLMs
- · Enterprises adopting AI agents
- · Inefficient LLM serving architectures
- · Companies with high AI operational costs
The TraceLab dataset will enable the creation of more specialized and efficient LLM serving architectures tailored for agentic workflows.
Improved efficiency in serving AI agents will reduce the cost of deployment, accelerating their integration into various industries and transforming white-collar work.
As AI agents become ubiquitous and cost-effective, their cumulative impact could lead to significant reconfigurations of labor markets and the overall digital economy.
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