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.
All prompts
17 prompts in this category
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.
Design an Orchestrator + Worker Multi-Agent System
Plan a supervisor agent that breaks a task into subtasks and delegates to specialized worker agents, then merges their results.
Best for
Get a clear topology, message contract, and merge strategy for a multi-agent system instead of an unpredictable swarm.
Run an Adversarial Reviewer Agent Over Another Agent's Output
Set up a critic agent that stress-tests a generator agent's work against explicit criteria before anything ships.
Best for
Catch errors, hallucinations, and missed requirements automatically by pairing a maker agent with a skeptical reviewer agent.
Define Handoff Contracts Between Specialized Agents
Write the exact input/output schemas and handoff rules so agents pass work cleanly without losing context.
Best for
Eliminate the most common multi-agent failure — broken handoffs — by making every transfer a typed, validated contract.
Design an MCP Server to Expose Internal Tools to an Agent
Plan a Model Context Protocol server that safely exposes your internal data and actions as tools an agent can call.
Best for
Get a concrete MCP server design — tools, resources, auth, and limits — so agents in Claude, Claude Code, or other MCP clients can use your systems.
Write Tool Descriptions a Model Will Actually Use Correctly
Rewrite ambiguous tool/function definitions so an agent reliably selects the right tool with the right arguments.
Best for
Reduce wrong-tool and bad-argument errors by making each tool's name, description, and parameter docs unambiguous to the model.
Build a Retrieval (RAG) Tool an Agent Can Query
Design a retrieval tool that returns grounded, citable chunks so an agent answers from your data instead of guessing.
Best for
Give an agent a clean retrieval interface — query in, ranked passages with sources out — that minimizes hallucination.
Brief a Coding Agent (Claude Code / Codex) on a Real Task
Write a precise task brief for an autonomous coding agent so it ships the right change without wandering across the codebase.
Best for
Get a self-contained brief — context, constraints, definition of done — that lets a coding agent work safely in an existing repo.
Plan-Then-Execute Mode for a Coding Agent
Force a coding agent to produce and confirm an implementation plan before it edits a single file.
Best for
Prevent expensive wrong turns by separating planning from execution and getting a checkpoint before changes land.
Turn a Coding Agent into a Test-First TDD Partner
Direct a coding agent to write failing tests first, then implement just enough code to pass them.
Best for
Get higher-quality, regression-resistant changes by enforcing a red-green-refactor loop with the agent.
Debug with an Agent Using a Hypothesis-Driven Loop
Guide a coding agent to debug systematically — form a hypothesis, add instrumentation, test it, narrow down — instead of random edits.
Best for
Find the real root cause faster by forcing structured, evidence-based debugging rather than shotgun fixes.
Build an Eval Suite for an Agent Before You Trust It
Create a structured evaluation set with test cases, scoring rubric, and pass thresholds so you measure an agent instead of vibe-checking it.
Best for
Replace "it seems to work" with repeatable evals that catch regressions when you change the prompt, model, or tools.
Add Guardrails Before an Agent Gets Write Access
Design layered guardrails — input filters, action confirmation, output checks, and kill switches — for an agent that can take real actions.
Best for
Make an action-taking agent safe to deploy by adding controls around what it can do, when, and with what oversight.
Diagnose Why an Agent Keeps Failing in Production
Analyze an agent's failing traces to classify root causes — prompt, tools, model, or data — and prescribe the highest-leverage fix.
Best for
Turn a pile of bad runs into a ranked list of root causes and concrete fixes instead of guessing what to tweak.
Every prompt here is free. The course teaches the thinking behind them.
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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.