diff --git a/benchmarks/benchmark_lib.sh b/benchmarks/benchmark_lib.sh
index 9a01f63b4b..a318b074ce 100644
--- a/benchmarks/benchmark_lib.sh
+++ b/benchmarks/benchmark_lib.sh
@@ -1797,7 +1797,11 @@ build_replay_cmd() {
     # X-Correlation-ID is useful tracing metadata but does not establish that
     # binding by itself. AIPerf emits nvext.session_control bind/close actions
     # keyed by the stable conversation correlation ID when this flag is set.
-    if [[ "${FRAMEWORK:-}" == dynamo-* ]]; then
+    # Opt-out: recipes set AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING=0 to skip this.
+    # aiperf's conv-aware routing emits nvext.session_control, a removed POC field
+    # (dynamo #9920 / v1.3.0-dev) that current dynamo builds reject with a 400
+    # (they moved to router/routing_constraints/agent_context). Default stays on.
+    if [[ "${FRAMEWORK:-}" == dynamo-* && "${AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING:-1}" != "0" ]]; then
         REPLAY_CMD+=" --use-dynamo-conv-aware-routing"
         # The upstream 300s affinity TTL is shorter than an overloaded
         # high-concurrency agentic request. Keep bindings alive across long
diff --git a/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/agg-gb200-tp8-agentic.yaml b/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/agg-gb200-tp8-agentic.yaml
new file mode 100644
index 0000000000..f70a713f7d
--- /dev/null
+++ b/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/agg-gb200-tp8-agentic.yaml
@@ -0,0 +1,159 @@
+name: "svf-vllm-agg-gb200-tp8-agentic"
+
+# AgentX low-concurrency aggregate recipe for DeepSeek-V4-Pro on GB200. One
+# TP8 vLLM worker spans two four-GPU nodes and serves both prefill and decode.
+
+model:
+  path: "deepseek-v4-pro"
+  container: "vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96"
+  precision: "fp4"
+
+dynamo:
+  hash: "1f74ef8c204ed6e283c22374f2b8c5cc83bf7b52"
+  install: true
+
+setup_script: vllm-container-deps.sh
+
+environment:
+  # The frontend shares Grace CPU capacity with the long TP8 cold start. Keep
+  # Dynamo discovery alive until the outer two-hour health check expires.
+  ETCD_LEASE_TTL: "7200"
+
+slurm:
+  time_limit: "8:00:00"
+
+health_check:
+  max_attempts: 720
+  interval_seconds: 10
+
+resources:
+  gpu_type: "gb200"
+  gpus_per_node: 4
+  agg_nodes: 2
+  agg_workers: 1
+  gpus_per_agg: 8
+
+infra:
+  etcd_nats_dedicated_node: false
+  nats_max_payload_mb: 32
+
+frontend:
+  type: dynamo
+  enable_multiple_frontends: false
+  args:
+    router-mode: "kv"
+    router-temperature: 0.0
+    router-queue-threshold: 65536
+    active-decode-blocks-threshold: "None"
+    active-prefill-tokens-threshold: "None"
+    active-prefill-tokens-threshold-frac: "None"
+
+backend:
+  type: vllm
+  connector: null
+  mooncake_kv_store:
+    store_config:
+      metadata_server: "P2PHANDSHAKE"
+      global_segment_size: "150GB"
+      local_buffer_size: "4GB"
+      protocol: "rdma"
+      # Pin to the IB compute fabric. device_name: "" auto-discovers RoCE/link-local
+      # NICs (mlx5_2/mlx5_5) that can not route cross-node and hang mooncake setup.
+      device_name: "mlx5_0,mlx5_1,mlx5_3,mlx5_4"
+      mode: "embedded"
+      enable_offload: false
+  aggregated_environment:
+    DYN_REQUEST_PLANE: "tcp"
+    VLLM_ENGINE_READY_TIMEOUT_S: "3600"
+    VLLM_RPC_TIMEOUT: "600000"
+    TILELANG_CLEANUP_TEMP_FILES: "1"
+    # Off: the c188b96 container's CUDA-graph capture path enters vLLM's
+    # nccl_symm_mem_context without set_graph_pool_id, so capture asserts
+    # "graph_pool_id is not set under graph capture". Re-enable once this recipe
+    # moves to a container built from a vLLM with the cuda_graph.py fix.
+    VLLM_USE_NCCL_SYMM_MEM: "0"
+    TORCH_SYMMMEM: "NVSHMEM"
+    NCCL_CUMEM_ENABLE: "1"
+    NCCL_MNNVL_ENABLE: "1"
+    NCCL_NVLS_ENABLE: "1"
+    VLLM_SERVER_DEV_MODE: "1"
+    VLLM_USE_V2_MODEL_RUNNER: "1"
+    VLLM_USE_RUST_FRONTEND: "1"
+    VLLM_ALLREDUCE_USE_FLASHINFER: "1"
+    VLLM_FLASHINFER_ALLREDUCE_BACKEND: "auto"
+    VLLM_MOONCAKE_LOAD_RECV_THREADS: "4"
+    VLLM_CONNECTOR_PREFETCH_DEPTH: "8"
+    VLLM_CONNECTOR_PREFETCH_KV_CAP: "0.65"
+    DG_JIT_CACHE_DIR: "/tmp/dg-cache-dsv4-agentx"
+    VLLM_DSV4_MEGA_FP8_COMBINE: "1"
+    VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS: "1800"
+    VLLM_PREFIX_CACHE_RETENTION_INTERVAL: "32768"
+    VLLM_SPARSE_INDEXER_MAX_LOGITS_MB: "1024"
+    UCX_MEMTYPE_CACHE: "n"
+    UCX_MEMTYPE_REG_WHOLE: "n"
+    UCX_RCACHE_MAX_UNRELEASED: "1024"
+    UCX_TLS: "cuda_copy,cuda_ipc,tcp"
+    NCCL_P2P_LEVEL: NVL
+    MC_ENABLE_DEST_DEVICE_AFFINITY: "1"
+    # MC_STORE_CLIENT_METRIC: "1"
+    # MC_STORE_CLIENT_METRIC_INTERVAL: "5"
+    # MC_TE_METRIC: "0"
+
+  vllm_config:
+    aggregated:
+      kv-transfer-config: '{"kv_connector":"MooncakeStoreConnector","kv_role":"kv_both","kv_connector_extra_config":{"load_async":true,"lookup_async":true,"enable_offload":false}}'
+      served-model-name: "deepseek-ai/DeepSeek-V4-Pro"
+      kv-cache-dtype: "fp8"
+      tensor-parallel-size: 8
+      pipeline-parallel-size: 1
+      disable-custom-all-reduce: true
+      enable-cumem-allocator: true
+      # numa-bind disabled: srt-slurm's cpus-per-task/cgroup already constrains
+      # CPU affinity, so vLLM's auto-NUMA detection bails and --numa-bind hard-
+      # errors ("could not detect the GPU-to-NUMA topology"). Re-enable only with
+      # an explicit --numa-bind-nodes mapping for this cluster.
+      # numa-bind: true
+      attention-config: '{"backend": "FLASHINFER_MLA_SPARSE_DSV4", "use_prefill_query_quantization": true, "use_fp4_indexer_cache": true}'
+      max-model-len: 1048576
+      # AgentX session fan-out can exceed the configured live-session
+      # concurrency; retain scheduler/graph headroom for subagent bursts.
+      max-num-seqs: 32
+      max-num-batched-tokens: 8192
+      trust-remote-code: true
+      no-enable-flashinfer-autotune: true
+      block-size: 256
+      compilation-config: '{"cudagraph_mode":"FULL_DECODE_ONLY","mode":0}'
+      max-cudagraph-capture-size: 32
+      gpu-memory-utilization: 0.95
+      kv-cache-memory: 37580963840  # 35 GiB
+      stream-interval: 10
+      no-disable-hybrid-kv-cache-manager: true
+      tokenizer-mode: deepseek_v4
+
+sbatch_directives:
+  cpus-per-task: "72"
+
+srun_options:
+  container-remap-root: ""
+
+benchmark:
+  type: custom
+  command: bash /infmax-workspace/benchmarks/multi_node/agentic_srt.sh
+  env:
+    INFMAX_CONTAINER_WORKSPACE: /infmax-workspace
+    RESULT_DIR: /logs/agentic
+    PORT: "8000"
+    # One aggregate worker owns all eight GPUs. Use the aggregate accounting
+    # path instead of summing independent prefill and decode allocations.
+    # Aggregate (IS_MULTINODE=false) uses the single-node topology labels below
+    # directly (num_gpus = TP), so they are set here rather than CI-injected.
+    IS_MULTINODE: "false"
+    TP: "8"
+    EP_SIZE: "1"
+    DP_ATTENTION: "false"
+    # Off: aiperf emits nvext.session_control, rejected by current dynamo (see benchmark_lib.sh).
+    AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING: "0"
+    AIPERF_DYNAMO_SESSION_TIMEOUT_SECONDS: "14400"
+    AIPERF_DATASET_MMAP_CACHE_DIR: "/aiperf_mmap_cache"
+    HF_HUB_CACHE: "/hf_hub_cache"
+    WEKA_LOADER_OVERRIDE: "semianalysis_cc_traces_weka_062126"
diff --git a/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/disagg-gb200-3p1d-dep8-dep16-agentic.yaml b/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/disagg-gb200-3p1d-dep8-dep16-agentic.yaml
new file mode 100644
index 0000000000..024bb22140
--- /dev/null
+++ b/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/disagg-gb200-3p1d-dep8-dep16-agentic.yaml
@@ -0,0 +1,195 @@
+name: "svf-vllm-disagg-gb200-3p1d-dep8-dep16-agentic"
+
+# Three DEP8 prefill workers feeding one DEP16 decode worker, with attention /
+# expert parallelism matching each role and hybrid load balancing.
+
+model:
+  path: "deepseek-v4-pro"
+  container: "vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96"
+  precision: "fp4"
+
+dynamo:
+  hash: "1f74ef8c204ed6e283c22374f2b8c5cc83bf7b52"
+  install: true
+
+setup_script: vllm-container-deps.sh
+
+environment:
+  # Apply the lease to the frontend as well as every prefill/decode worker so
+  # discovery survives the long CPU-saturated cold start.
+  ETCD_LEASE_TTL: "7200"
+
+slurm:
+  time_limit: "8:00:00"
+
+health_check:
+  max_attempts: 720
+  interval_seconds: 10
+
+resources:
+  gpu_type: "gb200"
+  gpus_per_node: 4
+  prefill_nodes: 6
+  decode_nodes: 4
+  prefill_workers: 3
+  decode_workers: 1
+  gpus_per_prefill: 8
+  gpus_per_decode: 16
+
+infra:
+  etcd_nats_dedicated_node: false
+  nats_max_payload_mb: 32
+
+frontend:
+  type: dynamo
+  enable_multiple_frontends: false
+  args:
+    router-mode: "kv"
+    router-temperature: 0.0
+    router-queue-threshold: 65536
+    active-decode-blocks-threshold: "None"
+    active-prefill-tokens-threshold: "None"
+    active-prefill-tokens-threshold-frac: "None"
+    tokenizer: "fastokens"
+
+backend:
+  type: vllm
+  dp_launch_mode: per_node
+  connector: null
+  kv_events_config:
+    prefill: true
+  mooncake_kv_store:
+    store_config:
+      metadata_server: "P2PHANDSHAKE"
+      global_segment_size: "150GB"
+      local_buffer_size: "4GB"
+      protocol: "rdma"
+      # Pin to the IB compute fabric. device_name: "" auto-discovers RoCE/link-local
+      # NICs (mlx5_2/mlx5_5) that can not route cross-node and hang mooncake setup.
+      device_name: "mlx5_0,mlx5_1,mlx5_3,mlx5_4"
+      mode: "embedded"
+      enable_offload: false
+  prefill_environment:
+    DG_JIT_CACHE_DIR: "/tmp/dg-cache-dsv4-agentx"
+    TORCH_SYMMMEM: "NVSHMEM"
+    VLLM_USE_NCCL_SYMM_MEM: "1"
+    VLLM_ENGINE_READY_TIMEOUT_S: "3600"
+    VLLM_RPC_TIMEOUT: "600000"
+    VLLM_LOG_STATS_INTERVAL: "1"
+    VLLM_V2_WARMUP_MAX_NUM_SEQS: "20"
+    VLLM_SERVER_DEV_MODE: "1"
+    VLLM_USE_V2_MODEL_RUNNER: "1"
+    VLLM_MOONCAKE_LOAD_RECV_THREADS: "20"
+    VLLM_CONNECTOR_PREFETCH_DEPTH: "8"
+    VLLM_CONNECTOR_PREFETCH_KV_CAP: "0.65"
+    VLLM_DSV4_MEGA_FP8_COMBINE: "1"
+    VLLM_ALLREDUCE_USE_SYMM_MEM: "0"
+    TILELANG_CLEANUP_TEMP_FILES: "1"
+    UCX_MEMTYPE_CACHE: "n"
+    UCX_TLS: "rc,cuda_copy"
+    NCCL_CUMEM_ENABLE: "1"
+    NCCL_MNNVL_ENABLE: "1"
+    NCCL_NVLS_ENABLE: "1"
+    VLLM_RANDOMIZE_DP_DUMMY_INPUTS: "1"
+    VLLM_PREFIX_CACHE_RETENTION_INTERVAL: "32768"
+    VLLM_USE_BREAKABLE_CUDAGRAPH: "0"
+    MC_ENABLE_DEST_DEVICE_AFFINITY: "1"
+    # MC_STORE_CLIENT_METRIC: "1"
+    # MC_STORE_CLIENT_METRIC_INTERVAL: "5"
+    # MC_TE_METRIC: "0"
+  decode_environment:
+    DG_JIT_CACHE_DIR: "/tmp/dg-cache-dsv4-agentx"
+    TORCH_SYMMMEM: "NVSHMEM"
+    VLLM_USE_NCCL_SYMM_MEM: "1"
+    VLLM_ENGINE_READY_TIMEOUT_S: "3600"
+    VLLM_RPC_TIMEOUT: "600000"
+    VLLM_LOG_STATS_INTERVAL: "1"
+    VLLM_V2_WARMUP_MAX_NUM_SEQS: "20"
+    VLLM_SERVER_DEV_MODE: "1"
+    VLLM_USE_V2_MODEL_RUNNER: "1"
+    VLLM_MOONCAKE_LOAD_RECV_THREADS: "20"
+    VLLM_DSV4_MEGA_FP8_COMBINE: "1"
+    VLLM_ALLREDUCE_USE_SYMM_MEM: "0"
+    TILELANG_CLEANUP_TEMP_FILES: "1"
+    UCX_MEMTYPE_CACHE: "n"
+    UCX_TLS: "rc,cuda_copy"
+    NCCL_CUMEM_ENABLE: "1"
+    NCCL_MNNVL_ENABLE: "1"
+    NCCL_NVLS_ENABLE: "1"
+    VLLM_RANDOMIZE_DP_DUMMY_INPUTS: "1"
+    VLLM_PREFIX_CACHE_RETENTION_INTERVAL: "32768"
+    MC_ENABLE_DEST_DEVICE_AFFINITY: "1"
+    # MC_STORE_CLIENT_METRIC: "1"
+    # MC_STORE_CLIENT_METRIC_INTERVAL: "5"
+    # MC_TE_METRIC: "0"
+
+  vllm_config:
+    prefill:
+      kv-transfer-config: '{"kv_connector":"MultiConnector","kv_role":"kv_both","kv_connector_extra_config":{"connectors":[{"kv_connector":"NixlConnector","kv_role":"kv_both","kv_load_failure_policy":"fail","kv_buffer_device":"cuda","kv_connector_extra_config":{"enforce_handshake_compat":false,"enable_cross_layers_blocks":false}},{"kv_connector":"MooncakeStoreConnector","kv_role":"kv_both","kv_connector_extra_config":{"load_async":true,"lookup_async":true,"enable_cross_layers_blocks":false,"enable_offload":false}}]}}'
+      served-model-name: "deepseek-ai/DeepSeek-V4-Pro"
+      kv-cache-dtype: "fp8"
+      tensor-parallel-size: 1
+      pipeline-parallel-size: 1
+      data-parallel-size: 8
+      data-parallel-rpc-port: 13345
+      enable-expert-parallel: true
+      enable-ep-weight-filter: true
+      enable-cumem-allocator: true
+      moe-backend: "deep_gemm_amxf4_mega_moe"
+      numa-bind: true
+      attention-config: '{"backend": "FLASHINFER_MLA_SPARSE_DSV4", "use_prefill_query_quantization": true, "use_fp4_indexer_cache": true}'
+      max-model-len: 1048576
+      max-num-seqs: 214
+      max-num-batched-tokens: 8192
+      trust-remote-code: true
+      no-enable-flashinfer-autotune: true
+      block-size: 256
+      gpu-memory-utilization: 0.9
+      no-disable-hybrid-kv-cache-manager: true
+      tokenizer-mode: deepseek_v4
+    decode:
+      kv-transfer-config: '{"kv_connector":"MultiConnector","kv_role":"kv_both","kv_connector_extra_config":{"connectors":[{"kv_connector":"NixlConnector","kv_role":"kv_both","kv_load_failure_policy":"fail","kv_buffer_device":"cuda","kv_connector_extra_config":{"enforce_handshake_compat":false,"enable_cross_layers_blocks":false}},{"kv_connector":"MooncakeStoreConnector","kv_role":"kv_both","kv_connector_extra_config":{"load_async":true,"lookup_async":true,"enable_cross_layers_blocks":false,"enable_offload":false}}]}}'
+      served-model-name: "deepseek-ai/DeepSeek-V4-Pro"
+      kv-cache-dtype: "fp8"
+      tensor-parallel-size: 1
+      pipeline-parallel-size: 1
+      data-parallel-size: 16
+      data-parallel-rpc-port: 13345
+      enable-expert-parallel: true
+      enable-ep-weight-filter: true
+      enable-cumem-allocator: true
+      moe-backend: "deep_gemm_amxf4_mega_moe"
+      numa-bind: true
+      attention-config: '{"backend": "FLASHINFER_MLA_SPARSE_DSV4", "use_prefill_query_quantization": true, "use_fp4_indexer_cache": true}'
+      max-model-len: 1048576
+      max-num-seqs: 160
+      max-num-batched-tokens: 256
+      trust-remote-code: true
+      no-enable-flashinfer-autotune: true
+      block-size: 256
+      compilation-config: '{"cudagraph_mode":"FULL_DECODE_ONLY","mode":0}'
+      max-cudagraph-capture-size: 160
+      gpu-memory-utilization: 0.95
+      no-disable-hybrid-kv-cache-manager: true
+      tokenizer-mode: deepseek_v4
+
+sbatch_directives:
+  cpus-per-task: "72"
+
+srun_options:
+  container-remap-root: ""
+
+benchmark:
+  type: custom
+  command: bash /infmax-workspace/benchmarks/multi_node/agentic_srt.sh
+  env:
+    INFMAX_CONTAINER_WORKSPACE: /infmax-workspace
+    RESULT_DIR: /logs/agentic
+    PORT: "8000"
+    IS_MULTINODE: "true"
+    # Off: aiperf emits nvext.session_control, rejected by current dynamo (see benchmark_lib.sh).
+    AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING: "0"
+    AIPERF_DYNAMO_SESSION_TIMEOUT_SECONDS: "14400"
+    AIPERF_DATASET_MMAP_CACHE_DIR: "/aiperf_mmap_cache"
+    HF_HUB_CACHE: "/hf_hub_cache"
+    WEKA_LOADER_OVERRIDE: "semianalysis_cc_traces_weka_062126"
diff --git a/configs/nvidia-master.yaml b/configs/nvidia-master.yaml
index a167f49270..2d02ff92d4 100644
--- a/configs/nvidia-master.yaml
+++ b/configs/nvidia-master.yaml
@@ -7881,6 +7881,65 @@ dsv4-fp4-gb200-dynamo-vllm-agentic-2p1d-dep8-dep8:
           ep: 8
           dp-attn: true
 
+dsv4-fp4-gb200-dynamo-vllm-agentic-agg:
+  image: vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96
+  model: deepseek-ai/DeepSeek-V4-Pro
+  model-prefix: dsv4
+  runner: cluster:gb200-nv
+  precision: fp4
+  framework: dynamo-vllm
+  router: { name: dynamo-router, version: "1.3.0.dev20260618" }
+  multinode: true
+  disagg: false
+  scenarios:
+    agentic-coding:
+    - search-space:
+      - spec-decoding: none
+        conc-list: [16]
+        prefill:
+          num-worker: 1
+          tp: 8
+          ep: 1
+          dp-attn: false
+          additional-settings:
+          - "CONFIG_FILE=recipes/vllm/deepseek-v4/agentic/agg-gb200-tp8-agentic.yaml"
+        # The aggregate worker also performs decode; keep the decode worker
+        # count at zero so result aggregation counts eight GPUs only once.
+        decode:
+          num-worker: 0
+          tp: 8
+          ep: 1
+          dp-attn: false
+
+dsv4-fp4-gb200-dynamo-vllm-agentic-3p1d-dep8-dep16:
+  image: vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96
+  model: deepseek-ai/DeepSeek-V4-Pro
+  model-prefix: dsv4
+  runner: cluster:gb200-nv
+  precision: fp4
+  framework: dynamo-vllm
+  router: { name: dynamo-router, version: "1.3.0.dev20260618" }
+  kv-p2p-transfer: nixl
+  multinode: true
+  disagg: true
+  scenarios:
+    agentic-coding:
+    - search-space:
+      - spec-decoding: none
+        conc-list: [1536]
+        prefill:
+          num-worker: 3
+          tp: 8
+          ep: 8
+          dp-attn: true
+          additional-settings:
+          - "CONFIG_FILE=recipes/vllm/deepseek-v4/agentic/disagg-gb200-3p1d-dep8-dep16-agentic.yaml"
+        decode:
+          num-worker: 1
+          tp: 16
+          ep: 16
+          dp-attn: true
+
 dsv4-fp4-gb300-dynamo-sglang-agentic-agg:
   image: lmsysorg/sglang:nightly-dev-cu13-20260711-7de33ce8
   model: deepseek-ai/DeepSeek-V4-Pro
diff --git a/perf-changelog.yaml b/perf-changelog.yaml
index 335245a6c3..c88a5cf965 100644
--- a/perf-changelog.yaml
+++ b/perf-changelog.yaml
@@ -5060,3 +5060,12 @@
     - "Re-pin VLLM_ROUTER_IMAGE to vllm/vllm-router:nightly-20260716-1fbcde7 (previous nightly-20260629-e667ebb was garbage-collected from Docker Hub)"
     - "Exclude known-bad nodes mia1-p01-g09,g14 from the disagg node pool"
   pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2301
+
+- config-keys:
+    - dsv4-fp4-gb200-dynamo-vllm-agentic-agg
+    - dsv4-fp4-gb200-dynamo-vllm-agentic-3p1d-dep8-dep16
+  description:
+    - "Add GB200 Dynamo-vLLM AgentX aggregate TP8 at conc [16] and disaggregated 3P/1D DEP8/DEP16 at conc [1536]."
+    - "Use NVIDIA/srt-slurm main with its upstream per-node DP, multi-node TP VLLM_PORT, Dynamo health-count, and Mooncake master fixes. TP8 uses FlashInfer all-reduce and disables vLLM custom all-reduce."
+    - "Image: vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96"
+  pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2302
diff --git a/runners/launch_gb200-nv.sh b/runners/launch_gb200-nv.sh
index a5f917da09..458dec6387 100755
--- a/runners/launch_gb200-nv.sh
+++ b/runners/launch_gb200-nv.sh
@@ -156,11 +156,15 @@ elif [[ $FRAMEWORK == "dynamo-vllm" ]]; then
         export MODEL_PATH="/mnt/lustre01/models/kimi-k2.5-nvfp4"
         export SRT_SLURM_MODEL_PREFIX="kimi-k2.5-nvfp4"
     elif [[ $MODEL_PREFIX == "dsv4" && $PRECISION == "fp4" ]]; then
-        # The FP4 checkpoint is staged on compute-visible Lustre. The former
-        # /mnt/numa1 path is no longer present on watchtower compute nodes;
-        # the lowercase Lustre sibling is the FP8 checkpoint, so keep the
-        # NVFP4 path explicit here.
-        export MODEL_PATH="/mnt/lustre01/models/DeepSeek-V4-Pro-NVFP4/"
+        if [[ "${IS_AGENTIC:-0}" == "1" ]]; then
+            # AgentX was tuned against the canonical DeepSeek-V4-Pro
+            # checkpoint, which is staged on compute-visible Lustre.
+            export MODEL_PATH="/mnt/lustre01/models/DeepSeek-V4-Pro/"
+        else
+            # Existing fixed-sequence GB200 recipes use the NVIDIA ModelOpt
+            # NVFP4 checkpoint.
+            export MODEL_PATH="/mnt/lustre01/models/DeepSeek-V4-Pro-NVFP4/"
+        fi
         export SRT_SLURM_MODEL_PREFIX="deepseek-v4-pro"
     elif [[ $MODEL_PREFIX == "minimaxm2.5" && $PRECISION == "fp4" ]]; then
         export MODEL_PATH="/mnt/lustre01/models/MiniMax-M2.5-NVFP4"
@@ -293,21 +297,15 @@ fi
 
 # TODO(CJQ): make first class upon srt-slurm upstream refactor
 if [[ "$IS_AGENTIC" == "1" ]]; then
-    # Agentic multi-node uses the same pinned cquil11/srt-slurm-nv commit as
-    # launch_gb300-nv.sh — everything the agentic recipes need is there:
-    #   - BenchmarkType.CUSTOM + benchmark.command + benchmark.env
-    #     (the hook that hands off to benchmarks/multi_node/agentic_srt.sh)
-    #   - DynamoConfig.wheel (recipes pin the ai-dynamo wheel)
-    #   - srtctl apply --no-preflight (model path /mnt/numa1 is compute-node
-    #     local NVMe, invisible to the login-node runner)
-    #   - benchmark_stage srun_options propagation (container-remap-root
-    #     must reach the agentic_srt.sh srun)
-    git clone https://github.com/cquil11/srt-slurm-nv.git "$SRT_REPO_DIR"
-    cd "$SRT_REPO_DIR"
-    git checkout de59739b172e507e15ebf145bfe305f606e82fbf
-    mkdir -p recipes/vllm/deepseek-v4/agentic
+    # Agentic recipes use NVIDIA/srt-slurm:main. The per-node DP launcher,
+    # matching Dynamo health counts, multi-node TP VLLM_PORT handling, and
+    # Mooncake master compatibility are all upstream.
+    git clone --branch main --single-branch https://github.com/NVIDIA/srt-slurm.git "$SRT_REPO_DIR" || exit 1
+    cd "$SRT_REPO_DIR" || exit 1
+
+    mkdir -p recipes/vllm/deepseek-v4/agentic || exit 1
     cp -rT "$GITHUB_WORKSPACE/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic" \
-        recipes/vllm/deepseek-v4/agentic
+        recipes/vllm/deepseek-v4/agentic || exit 1
 elif [[ $FRAMEWORK == "dynamo-vllm" && $MODEL_PREFIX == "dsv4" ]]; then
     git clone https://github.com/NVIDIA/srt-slurm.git "$SRT_REPO_DIR"
     cd "$SRT_REPO_DIR"