# AI Agent Architecture: Why Process-Level Resilience Beats Proxy Gateways

> Source: <https://dev.to/hhhfs9s7y9code/ai-agent-architecture-why-process-level-resilience-beats-proxy-gateways-1io6>
> Published: 2026-06-13 09:25:08+00:00

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.

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:**

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 |

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](https://github.com/hhhfs9s7y9-code/neuralbridge-sdk)

*NeuralBridge: Apache 2.0, 1 dependency, 375 KB.*