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.
2 min readupdated 2026-08-01
/ quick answer
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…
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. 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. In practice: 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. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
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.
Example
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.
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.
Why does Agent Architecture matter for AI and automation?
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. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.