Click any tab above to drill into each cloud layer, or click nodes in the diagram. High-throughput design principles: Async-first event backbone, horizontal autoscaling on all compute tiers, model routing by latency SLA, shared agent memory via globally distributed cache.
AWS high-throughput pattern: Kinesis enhanced fanout decouples ingestion from inference. Lambda handles sub-second synchronous tool calls (provisioned concurrency eliminates cold starts). ECS Fargate runs stateful, long-context agents. Step Functions Express Workflows orchestrate multi-step pipelines at 100k executions/sec. Bedrock model router selects Claude vs Titan vs Cohere based on latency SLA and task type.
Azure high-throughput pattern: Event Hubs Kafka-compatible interface lets agents reuse existing Kafka tooling without rewrite. Durable Functions orchestrate fan-out/fan-in patterns with native human-in-the-loop checkpoints. KEDA scales AKS pods directly from queue depth. Azure OpenAI PTUs eliminate the per-minute token quota ceiling for sustained high-throughput inference.
GCP high-throughput pattern: Pub/Sub ordering keys guarantee per-entity agent task ordering at unlimited scale. Vertex Reasoning Engine provides a fully managed agent runtime, removing infra overhead. Gemini Flash is the fastest model for high-frequency tool calls. Dataflow provides real-time stream enrichment before tasks reach the agent layer. Spanner global consistency ensures agent state is reliable across regions.
Cross-cloud fabric design: Private circuits (Direct Connect + ExpressRoute + Cloud Interconnect) avoid the public internet for all agent-to-agent and inter-service traffic. OIDC workload identity federation means no static API keys cross cloud boundaries. Kafka MirrorMaker 2 provides active/active event replication so the agent orchestrator is cloud-agnostic. All telemetry is unified via OpenTelemetry before shipping to Grafana Cloud, giving a single pane of glass for SLO tracking regardless of which cloud is serving a request.