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.
/ 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…
/ frequently asked
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.
Why does Agent Architecture matter?
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.
What should I learn after Agent Architecture?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →LangGraph Framework
LangGraph is a Python library built on LangChain that enables building stateful, multi-actor applications with LLMs by modeling agentic workflows as graphs. It allows for defining complex agent behaviors, including loops and conditional logic, crucial for advanced AI agent orchestration.
- →Terminal Coding Agent
A terminal coding agent is an AI system that integrates with and operates within a command-line interface (CLI) to assist developers with programming tasks. It allows users to generate, debug, explain, and refactor code directly from the terminal.
- →Codex CLI
Codex CLI refers to a conceptual command-line interface designed to interact with AI models like OpenAI's (now deprecated) Codex for coding assistance. It allows developers to generate, explain, or debug code directly from the terminal.
- →Agent-to-Agent Protocol (A2A)
An Agent-to-Agent (A2A) protocol defines the communication standards and rules that enable autonomous AI agents to interact and exchange information or services directly with each other without human intervention. This foundational concept is crucial for building complex, collaborative multi-agent systems.
Related workflows
Turn this into a repeatable process.
- →Design an Agent Architecture Before Writing Code
A one-page design process that prevents the most expensive agent rebuilds.
- →Build a Tier-1 Customer Support Agent
An agent that handles common tickets end-to-end and hands off the rest.
- →Multi-Agent Customer Support
This workflow outlines how CrewAI can be used to build a multi-agent customer support system that automates inquiry routing, resolution, and escalation, improving response times and customer satisfaction.
- →Setup Terminal Coding Agent
This workflow outlines the steps to set up and configure an AI coding agent that operates directly within a terminal environment. It enables developers to use AI for coding tasks without leaving their command-line interface.
Related tool stacks
The tools that run it in production.
- →Agent Architecture Stack
The minimum tooling to design, run and observe a production agent.
- →Agent Research Stack
Web-search-enabled agent for autonomous research tasks.
- →Multi-Agent Orchestration Stack (CrewAI)
This stack outlines the essential components for deploying a robust multi-agent system using CrewAI, focusing on agent definition, tool integration, and monitoring for collaborative task execution.
- →Terminal Coding Agent Stack
This stack outlines the essential components for building and operating an AI coding agent directly within a terminal environment. It combines an LLM API with development tools and execution environments.
Related prompts
Reusable prompts for this job.
- →Agent Architecture Spec Prompt
Turns a fuzzy agent idea into a reviewable five-layer architecture spec.
- →Multi-Agent Role Definition Prompt
Generates crisp role prompts and handoff contracts for a team of agents.
- →Crypto Agent Design Prompt
Specifies an AI crypto agent's tools, permissions, guardrails and evaluation before a single line is written.
- →Wallet Monitoring Agent Prompt
System prompt for a read-only agent that watches addresses, filters noise and reports only decision-relevant activity.
Related use cases
How people apply it, and what came out.
- →SaaS Cuts First-Response Time from 6h to 4min with an Agent
A 12-person SaaS shipped a tier-1 support agent and kept humans on exceptions only.
- →Ecommerce Team Runs a Multi-Agent Catalog Pipeline
Three agents enrich, write and QA 12,000 product listings.
Comparisons & alternatives
Pick between the options.
- →Single Agent vs Multi-Agent System
One well-equipped agent beats a crowd for most jobs; multi-agent wins on genuinely separable, parallel work.
- →RAG vs Long Context Windows
Is RAG obsolete now that context windows are 1M+ tokens? Not quite — here's when each wins.
- →AI Agent vs Trading Bot
A trading bot executes fixed rules deterministically; an AI agent interprets context and decides which steps to take — powerful for research, risky for execution.