AI 에이전트 아키텍처: 프로세스 수준의 복원력이 프록시 게이트웨이를 능가하는 이유

The Great AI Architecture Debate

When building reliable AI agents, there are two dominant approaches.

Approach A: Proxy Gateway (LiteLLM, Braintrust, etc.)

App sends request to Gateway Proxy which forwards to LLM Provider. Requires Docker, database, operations team.

Approach B: Embedded SDK (NeuralBridge)

App plus SDK sends directly to LLM Provider. One dependency, pip install.

The Hidden Cost of Gateways

Every proxy gateway adds 30-200ms of network latency per call. For an agent that makes 10 LLM calls, that is 300-2000ms of unnecessary overhead.

Latency breakdown:

• Gateway overhead: +30-200ms per call
• Docker infrastructure: +1-3 GB RAM
• Database operations: +PostgreSQL maintenance
• Ops overhead: +0.5 FTE

Why Embedding Wins

Embedded reliability eliminates the network hop:

Factor Gateway Embedded SDK Added latency 30-200ms ~0ms Dependencies Docker, DB, Redis 1 (httpx) Install size 500MB+ 375 KB Single point of failure Yes (proxy) No Ops cost High Zero

The Hybrid Reality

Gateways serve a purpose for centralized logging, auth, and rate limiting. But for latency-sensitive AI agents, embedding reliability directly in the process is strictly better.

The ideal stack: embedded SDK for reliability plus lightweight observability layer on top.

https://github.com/hhhfs9s7y9-code/neuralbridge-sdk

NeuralBridge: Apache 2.0, 1 dependency, 375 KB.