Design an Agent Architecture Before Writing Code
A one-page design process that prevents the most expensive agent rebuilds.
/ quick answer
Specify the five architectural layers first — model, loop, tools, memory, policy — and only then choose a framework. A one-page design process that prevents the most expensive agent rebuilds.
- 01Write the job in one sentence: input, output, success criteria, hard limits.
- 02Choose the loop: ReAct for open-ended tasks, plan-and-execute for repeatable ones.
- 03List tools with read/write classification. Every write tool needs an approval rule.
- 04Pick a memory strategy: none, per-session, or persistent vector/SQL store.
- 05Set the policy boundary: max steps, max spend, forbidden actions, escalation path.
- 06Define 10 eval cases before building. These become your regression suite.
- 07Only now pick the framework (LangGraph, CrewAI, AI SDK, custom loop).
/ frequently asked
What does the Design an Agent Architecture Before Writing Code workflow do?
Specify the five architectural layers first — model, loop, tools, memory, policy — and only then choose a framework.
What problem does Design an Agent Architecture Before Writing Code solve?
Teams start with a framework and a prompt, then discover three weeks in that the agent has no memory strategy, no failure path and no cost ceiling.
How many steps does Design an Agent Architecture Before Writing Code take?
7 steps. It starts with write the job in one sentence: input, output, success criteria, hard limits. and ends with only now pick the framework (langgraph, crewai, ai sdk, custom loop)..
How do I set up Design an Agent Architecture Before Writing Code step by step?
1) Write the job in one sentence: input, output, success criteria, hard limits. 2) Choose the loop: ReAct for open-ended tasks, plan-and-execute for repeatable ones. 3) List tools with read/write classification. Every write tool needs an approval rule. 4) Pick a memory strategy: none, per-session, or persistent vector/SQL store. 5) Set the policy boundary: max steps, max spend, forbidden actions, escalation path. 6) Define 10 eval cases before building. These become your regression suite. 7) Only now pick the framework (LangGraph, CrewAI, AI SDK, custom loop).
Which tools does Design an Agent Architecture Before Writing Code need?
It uses agent-architecture-stack — each linked below with its own node.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →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.
- →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).
- →Agent Memory
Persistent context that lets agents retain preferences, decisions, and prior work.
- →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.
Related workflows
Turn this into a repeatable process.
- →Automate Agent Task Delegation
This workflow automates the process of an initiating AI agent delegating a complex task to a specialized recipient AI agent using a standardized Agent-to-Agent (A2A) protocol, ensuring efficient task handoff and execution. It streamlines multi-agent collaboration for defined objectives.
- →Design Agent-to-Human Escalation
A handoff contract that gives humans everything they need in one screen.
- →AI Agent + Web3 Execution
Give an AI agent on-chain capability safely: read freely, propose openly, and sign only inside hard-coded limits.
Related tool stacks
The tools that run it in production.
- →Agent Architecture Stack
The minimum tooling to design, run and observe a production agent.
- →Multi-Agent Orchestration Stack
Tooling for coordinating several specialised agents with reliable handoffs.
- →AI Agent + Web3 Stack
Agent framework, MCP/API tools, blockchain data access and a limited signing layer — with policy enforced in code.
Related prompts
Reusable prompts for this job.
- →Agent Architecture Spec Prompt
Turns a fuzzy agent idea into a reviewable five-layer architecture spec.
- →Crypto Agent Design Prompt
Specifies an AI crypto agent's tools, permissions, guardrails and evaluation before a single line is written.
- →Multi-Agent Role Definition Prompt
Generates crisp role prompts and handoff contracts for a team of agents.
- →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.
- →Build An AI Crypto Research Agent
A read-only research agent produced daily briefings on a 30-token watchlist, cutting a 90-minute manual routine to a 10-minute review.
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.
- →LangGraph vs CrewAI vs AI SDK for Agents
LangGraph for stateful control, CrewAI for fast role-based teams, AI SDK for shipping agents inside a product.
- →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.