[bench] Add agentic-trace multi-turn dataset to bench_serving (#29215)

Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

docs_new/docs/developer_guide/bench_serving.mdx

@@ -66,6 +66,7 @@ Select with `--dataset-name`:
 - `generated-shared-prefix`: synthetic dataset with shared long system prompts and short questions
 - `mmmu`: samples from MMMU (Math split) and includes images
 - `speed-bench`: [SPEED-Bench](https://huggingface.co/datasets/nvidia/SPEED-Bench) (**SPEculative Evaluation Dataset**) — a unified benchmark for evaluating [Speculative Decoding (SD)](https://arxiv.org/abs/2604.09557) algorithms. Uses the Throughput split, which provides fixed-length input sequences (1K–32K tokens) grouped into three output-entropy categories (`low_entropy`, `mixed`, `high_entropy`). Requires a pre-downloaded JSONL file passed via `--dataset-path`.
+- `agentic-trace`: replays pre-built multi-turn agentic traces (e.g. OpenHands / SWE-smith). Each conversation is replayed round by round, feeding the server's real assistant reply back into the next round's history. Requires a chat backend (`--backend sglang-oai-chat`) and a trace JSON passed via `--dataset-path`.
 
 Common dataset flags:
 
@@ -93,6 +94,13 @@ Image dataset flags (for `image`):
 - `--image-format`: Image format (jpeg or png)
 - `--image-content`: Image content type (random or blank)
 
+Agentic trace flags (for `agentic-trace`):
+
+- `--dataset-path`: path to the pre-built trace JSON
+- `--sharegpt-output-len`: per-turn output length (default: 220)
+- `--dataset-offset`: rotate the conversation list by this many entries before sampling, so successive sweep steps start on fresh conversations
+- `--agentic-max-turns`: cap each conversation to at most this many turns (useful for small, fast profiling runs)
+
 SPEED-Bench flags (for `speed-bench`):
 
 - `--dataset-path PATH`: path to the pre-downloaded SPEED-Bench Throughput JSONL (e.g., `throughput_1k.jsonl`). Use the [SPEED-Bench measurement framework](https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/specdec_bench) to generate it.