Architecture Patterns For LLM Systems
Reliable LLM products usually converge on a few core patterns: a request layer, a context layer, an action layer, and a control plane around them.
Use A Request Broker
A clean LLM system often starts with a request broker. This layer receives the user input, identifies the task type, applies authentication and rate limits, and decides which downstream path should handle the request. It sounds ordinary, but it prevents a lot of architectural drift. Without a broker, every feature invents its own routing logic and prompt assembly path. With one, the system gains a single place to enforce policies, attach metadata, and record trace identifiers.
The broker also makes it easier to handle mixed workloads. Some requests need retrieval. Others need classification, extraction, or tool use. A single entry layer can route each request toward the right path without forcing every workflow through the same heavy pipeline. That flexibility matters as the product grows beyond one headline use case.
Treat Context As Its Own Layer
The next pattern is a dedicated context layer. This is where retrieval, filtering, reranking, conversation history shaping, and metadata injection happen. Keeping context logic separate from prompt templates pays off quickly because teams can improve retrieval quality without rewriting response formatting and can test ranking changes independently. Context is too important to hide as helper code inside a prompt builder.
This layer is also where access control usually belongs. If a user should only see documents from one workspace or business unit, that filter needs to happen before the model ever sees the content. Mixing permission checks into the generation step is risky and harder to audit. The context layer gives the system a cleaner boundary for those controls.
Build A Controlled Action Plane
When systems need tool use, we prefer a controlled action plane rather than unrestricted agent autonomy. The model can choose from a well-defined set of actions, but each action runs through validation, logging, and optional approval checks. That makes tool use observable and reversible. It also lets the engineering team evolve tool schemas and rate limits without changing the rest of the application.
A controlled action plane is especially useful for workflows that touch external systems such as GitHub, ticketing platforms, CRM data, or incident tooling. These integrations carry operational risk. They need strict input validation, clear ownership, and reliable error handling. The model can still add value by deciding which action is relevant and how to summarize the result, but the action itself should remain inside a governed system boundary.
Add A Control Plane Around Everything
The final pattern is a control plane that manages prompts, evaluations, traces, rollout policies, and feature flags. This is the layer that makes the system operable as a product. It lets teams version prompt templates, compare model variants, stage releases, and inspect regressions without touching the request path for every change. Once multiple AI features exist, this control plane becomes essential.
These patterns are not flashy, but they are what make LLM systems durable. A request broker keeps the entry point clean. A context layer improves grounding. A controlled action plane governs side effects. A control plane makes the whole stack measurable. Most production systems eventually converge on these ideas because they reduce ambiguity and make the product easier to evolve without breaking trust.