AI Agents AI prompts
Agentic workflows, multi-agent systems, MCP tools, Claude Code and autonomous AI assistants. Best for designing reliable single and multi-agent systems, wiring tools, MCP servers, and coding agents like Claude Code, adding evaluation and guardrails before agents touch production.
17
In this category
All of them
Free to copy
5
Subcategories
3
Difficulty levels
Every prompt below is open. Copy it straight into ChatGPT, Claude, or Gemini.
Guide
AI agents guide
Open the guide that shows how this prompt category fits into a broader workflow.
Role path
Agent builder path
See how this category fits into a role-based AI workflow instead of using prompts in isolation.
Generator
Generate prompt variants
Use the prompt generator when you know the job to be done but not the exact prompt structure yet.
Course path
Course library
Move into structured lessons when you want a repeatable system, not only a single prompt.
Choose the closest workflow
Filter by subcategory and difficulty first. That usually gets you to the right prompt faster than scrolling the full list.
Filtered prompts
4 prompts match the current filters
Design a Single-Purpose Agent Spec from a Messy Workflow
Turn a vague "the AI should just handle this" request into a tight agent specification with scope, tools, stop conditions, and success criteria.
Best for
Produce a build-ready agent spec so an engineer can implement it in Claude Code, the OpenAI Agents SDK, or a framework like LangGraph without guessing.
Write a Robust System Prompt for a Customer-Facing Agent
Generate a production-grade system prompt with role, tone, tool-use rules, refusal policy, and escalation path for an agent that talks to real users.
Best for
Get a system prompt that holds up under adversarial users, ambiguous requests, and edge cases instead of one that only works in the happy path.
Decompose a Goal into a ReAct Reasoning-and-Tool Loop
Plan how an agent should alternate between reasoning and tool calls to reach a goal, with explicit halting logic to avoid infinite loops.
Best for
Design the think→act→observe loop for a task so the agent makes progress every step and knows exactly when it is done.
Add Long-Term Memory to a Conversational Agent
Design a memory layer so an agent remembers user preferences and prior decisions across sessions without bloating the context window.
Best for
Decide what to remember, where to store it, and how to retrieve only the relevant memories at the right moment.
Every prompt here is free. The course teaches the thinking behind them.
Copy as many prompts as you like. When you want to move from single prompts to a repeatable AI workflow, Learn AI in 30 Days walks through it, one day at a time.
Buy the course once ($15/$20 by length), or go all-access for $10/mo with a verifiable certificate.
Related categories
Explore adjacent prompt libraries that support ai agents workflows.
AI Automation
n8n/Make workflows, AI agents, backoffice automation and customer service automation
Programming & Dev
Code, debugging, architecture, DevOps and AI-powered development
Personal Productivity
Organization, automation, focus, efficient meetings and decision-making
Data Analysis
Excel, SQL, data visualization, KPIs and AI-powered reporting
How to use AI Agents prompts well
Start with the prompt closest to your workflow, replace any placeholders with your own context, and tell the model what a good output looks like. The fastest improvement usually comes from clearer context, tighter constraints, and a more specific deliverable.
This category is especially useful for designing reliable single and multi-agent systems. Treat the prompt as the execution layer, then refine it into a reusable workflow once you know it solves a real recurring problem.