Architecture Explained: Concepts, Workflows and Tools
Coordinating multiple model calls, tools, and data sources into one reliable 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.