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
Fine-Tuning Pipeline
AI Infrastructure
Real-Time Recommendation Pipeline
AI Infrastructure
RAG AI Knowledge Base
OpenAI Pattern
Vector Database System
AI Infrastructure
Model Serving Platform
AI Infrastructure
Multi-Agent AI System
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