| Embedding Model Server | AI & Agent Systems | Receives concurrent batches of text chunks from an ingestion pipeline, utilizes GPU tensor cores with dynamic micro-batching to generate 1536-dimensional normalized floating-point vectors, and returns embeddings in under 15 milliseconds. | View→ |
| Evaluation | AI & Agent Systems | Rigorously scores AI outputs against baseline truth to prevent regressions. | View→ |
| Hybrid Search Engine | AI & Agent Systems | Executes parallel queries across inverted text indexes and vector graph indexes, normalizes score distributions using Reciprocal Rank Fusion (RRF) or cross-encoder rerankers, and returns unified ranked search results in sub-50ms. | View→ |
| Input Guardrail | AI & Agent Systems | Inspects natural language prompt tokens through deterministic signature engines and low-latency transformer classifiers, sanitizing PII and validating structural syntax in single-digit milliseconds. | View→ |
| KV Cache Manager | AI & Agent Systems | During the initial prompt prefill phase, the model computes Key and Value attention matrices for all input tokens. The KV Cache Manager stores these tensors in non-contiguous physical GPU memory pages (PagedAttention). During subsequent autoregressive decoding steps, new tokens attend to historical tokens by retrieving cached KV tensors directly from memory rather than recomputing them, dramatically accelerating generation throughput. | View→ |
| LLM Evaluator | AI & Agent Systems | Ingests test prompts and model completions, applies standardized evaluation rubrics using heuristic metrics and high-capability judge models, calculates multidimensional quality scores (relevance, accuracy, toxicity, tone), and exports structured telemetry to guide model promotion. | View→ |
| LLM Gateway | AI & Agent Systems | Receives unified inference requests from internal applications, verifies tenant credentials, checks semantic cache for instant responses, enforces per-tenant token budgets, dispatches streaming or batch requests to the optimal model provider, and captures telemetry for cost and latency analysis. | View→ |
| Model Context Protocol Server | AI & Agent Systems | Initializes protocol handshakes with LLM host clients (such as IDEs or AI agents), advertises available tools and contextual resources via standardized JSON-RPC schemas, executes authorized tool actions upon client request, and streams structured results back to the agent. | View→ |
| Model Registry | AI & Agent Systems | Data scientists and training pipelines register trained model checkpoints alongside evaluation metrics, hyperparameters, and tensor signature schemas. The registry hashes artifacts, verifies signatures, and manages promotion workflows across Dev, Staging, and Production stages. | View→ |
| Model Serving Platform | AI & Agent Systems | When an inference request arrives, the model serving platform receives raw payload bytes, validates input schema against the model signature, and places the request into a dynamic batching queue. A worker thread batches concurrent requests together to maximize parallel GPU/TPU compute utilization, copies tensor data into hardware accelerator VRAM, runs the forward mathematical pass through serialized neural network weights (e.g., TensorRT, ONNX, PyTorch), performs output post-processing, and returns the prediction tensor with sub-100ms latency while dynamically autoscaling worker instances according to concurrency metrics. | View→ |
| Multi-Agent Framework | AI & Agent Systems | A coordinator agent decomposes an overarching user goal into a directed acyclic graph (DAG) of sub-tasks. It dispatches individual sub-tasks to specialized worker agents (e.g., researcher, coder, reviewer), provides each agent with targeted memory context and executable tool interfaces, manages inter-agent dialogue and voting consensus, validates execution constraints, and synthesizes intermediate outputs into a verified final solution. | View→ |
| Multimodal Ingestion Pipeline | AI & Agent Systems | The pipeline consumes raw multi-format files from storage streams, partitions files by media type, routes audio to automatic speech recognition (ASR) engines, executes optical character recognition (OCR) and layout analysis on documents, extracts keyframes from video streams, aligns temporal and visual metadata, generates unified cross-modal embeddings using foundation models (e.g., CLIP, Whisper), and indexes the vectors into vector databases alongside relational metadata. | View→ |
