How Architecture Actually Works
Coordinating multiple model calls, tools, and data sources into one reliable system. LLM Orchestration is the control layer that decides which model runs, in what order, with what context, and how outputs flow between steps — turning isolated prompts into a production system. This guide pulls together everything on Onexial tagged architecture — 9 connected nodes across definitions, workflows, tool stacks, comparisons, prompts and applied use cases — and orders it the way you would actually learn it: vocabulary first, then process, then tooling, then execution. Every item below links to a full node with its own examples and connections, so you can go as deep as you need without losing the map.
Core concepts behind Architecture
Before wiring anything together, the vocabulary has to be precise. These 4 definitions cover the terms that show up in almost every Architecture discussion — each one links to a full entry with an example and its own connections inside the graph.
LLM Orchestration
Coordinating multiple model calls, tools, and data sources into one reliable system.
Agent Architecture
Agent architecture is the structural blueprint of an AI agent: the model, the planning loop, the tools, the memory layer and the guardrails that decide how it acts.
Multi-Agent System
A multi-agent system splits a job across several specialised AI agents that coordinate through a shared plan, message bus or orchestrator.
Agent Planning (ReAct, Plan-and-Execute)
Agent planning is how an AI agent decides its next step — reactively step-by-step (ReAct) or by drafting a full plan up front (plan-and-execute).
Workflows: how Architecture runs end to end
Concepts only matter once they become a repeatable process. Below are 1 documented workflows that apply Architecture to a concrete problem, with the steps, the tools involved and the variations worth testing.
The Architecture tool stack
A stack is a set of tools chosen for one job, not a list of favourites. These 1 stacks show which combinations hold up in production for Architecture, and what each layer is actually responsible for.
Trade-offs and comparisons
Most Architecture decisions are trade-offs rather than right answers. These 2 comparisons break down the real differences, when each option wins, and the recommendation for the common case.
RAG vs Long Context Windows
Is RAG obsolete now that context windows are 1M+ tokens? Not quite — here's when each wins.
Single Agent vs Multi-Agent System
One well-equipped agent beats a crowd for most jobs; multi-agent wins on genuinely separable, parallel work.
Prompts you can reuse
Prompts are reusable components. Each of these 1 prompts is written to be dropped into a Architecture workflow with minimal editing, including the context it expects and an example output.
Frequently asked questions
- What is LLM Orchestration?
- LLM Orchestration is the control layer that decides which model runs, in what order, with what context, and how outputs flow between steps — turning isolated prompts into a production system.
- What is an example of LLM Orchestration?
- A pipeline routes simple questions to a fast model, escalates complex ones to a reasoning model, and falls back to a human when confidence is low.
- Why does LLM Orchestration matter for AI and automation?
- Coordinating multiple model calls, tools, and data sources into one reliable system. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
- What is the difference in RAG vs Long Context Windows?
- Massive context windows changed the calculus but didn't kill RAG. Cost, latency, freshness and precision all still favor retrieval for most production workloads.
- What are the main points of comparison?
- Cost per query: RAG: cheap, only relevant chunks vs Long context: expensive, full doc every time · Latency: RAG: fast (~1s retrieval + gen) vs Long context: slow (multi-second prefill) · Freshness: Update the index, done vs Re-send everything each request · Precision: Better with good chunking + reranking vs 'Lost in the middle' still real · Setup complexity: Higher (embedding, storage, retrieval) vs Just paste and prompt
- Which one should I choose?
- Use long context to prototype fast. Move to RAG once you cross ~1k queries/day or ~10 documents.
- What is Agent Architecture?
- Agent architecture describes how the parts of an autonomous AI system fit together. A minimal architecture has five layers: a reasoning model, a control loop (plan → act → observe → repeat), a tool interface, a memory store, and a policy layer that limits what the agent may do. Everything else — routers, sub-agents, evaluators, retries — is a variation on those five.
- What is an example of Agent Architecture?
- A support agent: Claude as the model, a ReAct loop capped at 12 steps, MCP tools for the CRM, pgvector for long-term memory, and a rule that refunds above $100 require human approval.