AgentX 1.0 Brings Realism to Inference Benchmarks

AgentX is a meaningful upgrade for inference benchmarking because it tests the workload shape modern coding agents actually create, not isolated prompt-and-response throughput.

• –Replaces fixed-sequence tests with closed-loop sessions featuring growing context, KV-cache reuse, tool delays, and parallel subagents.
• –The 393-session dataset has a 142k-token median input and 44% subagent participation, stressing cache capacity, offload bandwidth, scheduling, and routing.
• –Privacy-preserving synthetic payloads retain workload structure without exposing original prompts, code, or tool results.
• –AgentX measures serving performance—not coding correctness—so teams should pair it with task benchmarks such as SWE-bench.
• –Its common methodology gives vLLM, SGLang, and other stacks a reproducible target for validating real agentic-inference optimizations.