Agent Architecture Spec Prompt
Turns a fuzzy agent idea into a reviewable five-layer architecture spec.
/ quick answer
Run before building any agent. Feed the output to your team (or your coding agent) as the implementation brief. Turns a fuzzy agent idea into a reviewable five-layer architecture spec.
You are an AI agent architect. I will describe a job I want an agent to do. Return a specification with exactly these sections:
1. JOB CONTRACT — input, output, success criteria, hard limits.
2. LOOP — ReAct, plan-and-execute or hybrid, with justification and a max step count.
3. TOOLS — table of tool name, purpose, read/write, approval requirement.
4. MEMORY — what must persist, where it lives, retention and retrieval strategy.
5. POLICY — forbidden actions, spend ceiling, escalation triggers, kill switch.
6. FAILURE MODES — top 5 ways this agent fails and the mitigation for each.
7. EVAL SET — 10 concrete test cases with expected outcomes.
Be specific and opinionated. Flag anything that should not be an agent at all.
Job: {{JOB_DESCRIPTION}}
Systems available: {{SYSTEMS}}
Risk tolerance: {{RISK}}1. JOB CONTRACT — Input: inbound support email. Output: drafted reply + ticket tags. Success: 90% sent without edit...
/ frequently asked
What does the Agent Architecture Spec Prompt prompt do?
Run before building any agent. Feed the output to your team (or your coding agent) as the implementation brief.
Which AI models work with this prompt?
It is model-agnostic: it works with any capable general model. Replace the bracketed variables with your own context before running it.
How do I use this prompt?
1) Copy the prompt with the copy button. 2) Replace every bracketed variable with your real details. 3) Paste it into your AI assistant. 4) Compare the answer with the example output and ask for changes if needed.
What output should I expect?
1. JOB CONTRACT — Input: inbound support email. Output: drafted reply + ticket tags. Success: 90% sent without edit...
/ 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).
- →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-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.
- →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.
- →Build a Multi-Agent Content Team
Researcher, writer and editor agents under a supervisor, producing publishable drafts.
- →Cut Agent Costs by 60% Without Losing Quality
A measurable cost-reduction pass for any agent already in production.
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.
Related prompts
Reusable prompts for this job.
- →Multi-Agent Role Definition Prompt
Generates crisp role prompts and handoff contracts for a team of agents.
- →Autonomy Boundary Prompt
Defines exactly what an agent may do alone, with approval, or never.
- →AI Employee Job Description Prompt
Writes the role spec, KPIs and review cadence for an agent that owns a function.
- →Coding Agent Task Spec Prompt
Writes the task brief that keeps a coding agent inside scope.
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.
- →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.