CS²B | Enterprise AI Agent Development

FULL STACK ARCHITECTURE

From Application to Silicon

Ten layers of abstraction between your question and the transistors that answer it. Click any layer to explore.

APPLICATION

User-Facing Applications

Chat interfaces, dashboards, workflow automations, decision support systems. Where humans interact with agents.

React / Next.js REST / GraphQL / gRPC WebSocket Streaming Voice (sub-100ms)

Latency target < 200ms E2E

▼user intent / structured queries

AGENT
ORCHESTRATION

Agentic Runtime & Multi-Agent Coordination

Query decomposition, tool selection, multi-step planning, ReAct loops, human-in-the-loop routing. The "brain" that decides what to do and in what order.

LangGraph CrewAI MCP / A2A DSPy agent-sdk (Rust)

Security overhead < 10ms / call 20+ threat vectors

▼tool calls / retrieval requests / LLM prompts

RAG &
KNOWLEDGE

Retrieval, Knowledge Graphs & Verification

6-channel hybrid retrieval (Plugin, HNSW, BM25, Graph, Table Discovery, Ontology-Typed), 4-tier Knowledge Graph, Verify-then-Trust loop (DeepSeek → Z3 → Vadalog).

LAKEer FUSION Neo4j / Milvus PPR K=60 RRF Z3 / Vadalog

FACTS Grounding 77.7% Hallucination: 0.3%

▼verified context + token-budgeted prompt

MODEL
LAYER

Foundation Models & Fine-Tuning

Decoder-only Transformers. Pre-training (next-token prediction) → SFT (instruction tuning) → Alignment (RLHF/DPO). Model-agnostic: swap models without changing the stack.

Llama 3.x DeepSeek Nemotron LoRA / QLoRA Claude / GPT-4o

Parameters 8B — 405B Multi-model concurrent

COMPILATION BOUNDARY

INFERENCE
ENGINE

Serving Runtime & Scheduling

Continuous batching, PagedAttention, prefill/decode disaggregation, speculative decoding, KV cache management, tensor/pipeline parallelism across GPUs.

vLLM TensorRT-LLM Triton Server SGLang FlashAttention-3

P50 latency 87ms GPU util: 94%

▼optimized execution plan / fused kernels

ML COMPILER
& GRAPH OPT

Compilation, Graph Optimization & Codegen

Hardware-agnostic passes (op fusion, CSE, dead code elim) → hardware-specific passes (tensor core mapping, tiling, memory coalescing). IR lowering from graph → loop nests → hardware instructions. Quantization-aware compilation (INT4/FP8 mixed precision).

MLIR / LLVM TVM XLA IREE TensorRT ONNX

Graph reduction 35-45% Op fusion + tiling

▼tiled kernels / DMA commands / instruction schedule

RUNTIME
& DRIVERS

GPU/NPU Runtime, CUDA, Drivers

CUDA runtime, kernel launch, stream management, memory allocation (cudaMalloc / cuMem), NCCL collective operations, driver-level GPU scheduling, PCIe/NVLink DMA.

CUDA 12+ ROCm NCCL cuBLAS / cuDNN CUTLASS

Kernel launch ~5μs Stream concurrency

HARDWARE BOUNDARY

INTERCONNECT
& MEMORY

NVLink, InfiniBand, HBM, Memory Hierarchy

Intra-node: NVLink (1.8 TB/s per NVL72 domain). Inter-node: InfiniBand NDR (400 Gb/s). Memory: HBM3e (8 TB/s bandwidth, 192GB/GPU on B200). NUMA topology, PCIe Gen5, CXL.

NVLink 5 NVSwitch IB NDR 400G HBM3e PCIe Gen5

NVLink BW 1.8 TB/s HBM: 8 TB/s

▼DMA transfers / memory-mapped I/O / RDMA

GPU / NPU
ARCHITECTURE

Streaming Multiprocessors, Tensor Cores, Warps

B200: 192 SMs, 5th-gen Tensor Cores (FP4/FP8/INT8), 128 warps/SM, 32 threads/warp. Warp scheduling, register file (256KB/SM), shared memory (228KB/SM), L2 cache (96MB). Instruction pipeline: fetch → decode → schedule → execute → writeback.

B200 Blackwell H100 Hopper MI350X CDNA 4 Gaudi 3 Custom NPU / DSP

B200 FP4 20 PFLOPS 192 SMs, 192GB HBM

▼electrical signals / clock domains

SILICON

Transistors, Process Nodes, Package

TSMC 4nm (B200), chiplet / multi-die packaging (2x Blackwell dies + Grace CPU), CoWoS (Chip-on-Wafer-on-Substrate) for HBM stacking, power delivery (1000W TDP), thermal management, billions of transistors switching at GHz frequencies.

TSMC 4nm 208B transistors CoWoS Chiplet / MCM 1000W TDP

Transistors 208 Billion Die: 2x 814mm²

We build AI agents
that work in production.

From Application to Silicon

Agentic AI — Full Stack Architecture

From Application to Silicon

We build AI agentsthat work in production.

From Application to Silicon

Agentic AI — Full Stack Architecture

From Application to Silicon

We build AI agents
that work in production.