Agent workloads: one user can create many requests

Picture it

An autonomous agent isn't a single, straightforward sprint; it's an expanding tree of exploratory branches, where each step spawns new questions, tool calls, and waiting periods. As the agent navigates this labyrinth, it leaves a long, sprawling trail of context in the system's memory. Serving these agents requires delicately managing this lingering footprint, ensuring that long pauses for tool use don't monopolize the GPU, and placing strict boundaries so a confused agent doesn't wander in an infinite loop forever.

What to understand

• Session affinity, context growth, tool latency, and parallel branches
• Task budgets, durable state, cancellation, idempotent tools, and loop detection

Follow the references

1. 1 · Engineering article

   Anthropic · Building effective agents ↗

   Map each workflow pattern to the number of model/tool calls and its failure path.

2. 2 · Documentation

   NVIDIA Dynamo · Router design ↗

   Relate repeated contexts and session traffic to cache reuse and load.

Make it concrete

Trace an end-to-end agent task. Report success, total tokens, tool time, cache reuse, and total cost. Add a task-wide deadline and verify all branches stop.