AI Infrastructure
Multi-agent AI systems decompose complex tasks across specialized agents — a planner agent breaks down the problem, domain-specific agents execute subtasks, and a supervisor agent orchestrates the workflow. This architecture supports tool use (web search, code execution, API calls), shared memory for context passing between agents, and human-in-the-loop checkpoints for critical decisions. Suited for AI teams decomposing complex reasoning tasks across specialized agents with tool use and human-in-the-loop checkpoints.
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Each agent type runs as an independent ECS service that scales based on queue depth. SQS provides durable task routing between agents with dead letter queues for failed tasks. Shared memory uses DynamoDB for persistent state and ElastiCache for fast context lookups within a session. Bedrock provides managed LLM inference that scales automatically without GPU provisioning.
LLM Inference Pipeline
AI Infrastructure
Production LLM serving on Azure with AKS GPU workers, request batching, KV cache management, and streaming token delivery.
Fine-Tuning Pipeline
AI Infrastructure
End-to-end ML fine-tuning pipeline on GCP with Vertex AI, Dataproc preprocessing, distributed training, and model registry.
Real-Time Recommendation Pipeline
AI Infrastructure
Low-latency recommendation engine combining collaborative filtering, content-based signals, and real-time user behavior for sub-50ms scoring.
RAG AI Knowledge Base
OpenAI Pattern
Retrieval-Augmented Generation pipeline with vector search, embedding generation, and LLM orchestration for enterprise AI apps.
Vector Database System
AI Infrastructure
Purpose-built vector database on OCI with HNSW indexing, hybrid search, metadata filtering, and multi-tenant isolation using OKE and Autonomous Database.
Model Serving Platform
AI Infrastructure
Multi-model serving platform on OCI with canary deployments via OKE, A/B testing, OCI Cache feature store, and automatic model rollback.
Multi-Agent AI System
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