Agent Task Decomposition Planner
Breaks a broad goal into a structured set of sub-tasks an AI agent can execute step by step, with clear success criteria and handoff points for human review.
Developers and product builders designing agent workflows (using frameworks like Claude Agent SDK, LangChain, or custom orchestration) who need a clear, checkable task plan before writing the actual agent logic.
You are an AI agent architect who designs reliable, well-scoped task plans for autonomous or semi-autonomous agents.
High-level goal for the agent: {{goal_description}}
Tools/capabilities the agent has access to: {{available_tools}}
Constraints (what it must NOT do without approval): {{constraints}}
Do the following:
1. Break the goal into a sequential list of discrete sub-tasks, each specific enough that the agent knows exactly what 'done' looks like for that step.
2. For each sub-task, specify: the exact tool(s) needed, the expected output/artifact, and a clear success condition the agent (or a human) can check against.
3. Identify which sub-tasks are safe for the agent to complete fully autonomously, and which ones require a human-in-the-loop checkpoint before proceeding (e.g., anything irreversible, costly, or touching sensitive data).
4. Note likely failure points in the plan (steps where the agent is most likely to get stuck, loop, or need a fallback) and suggest a fallback action for each.How to use
- Describe the agent's overall goal and exactly which tools or APIs it has access to.
- List any hard constraints on what the agent must not do without approval.
- Use the sub-task breakdown as the literal step sequence to implement in your agent's logic or prompt chain.
- Build human-in-the-loop checkpoints at exactly the points flagged before deploying the agent for real use.
Example input
Goal: Research 20 potential leads for a B2B SaaS product and produce a ranked shortlist with contact info. Tools: web search, a CRM API, a spreadsheet writer. Constraints: Must not send any outreach messages without human approval.
Example output
A sequential plan including sub-tasks like 'search for companies matching the ICP criteria' (web search, output: list of 30+ candidate companies, success: each has a verifiable website), 'enrich with contact info' (CRM API, output: contact records), and 'rank and write to spreadsheet' (spreadsheet writer, output: ranked shortlist file), with a flag that any step touching the CRM's write access needs human approval, and a noted failure point around ambiguous ICP matches with a fallback to flag for manual review instead of guessing.
Customization tips
- — If you're using a specific agent framework, mention it so tool references match that framework's terminology.
- — For multi-agent systems, ask for the plan to specify which sub-tasks should be handled by which specialized sub-agent.
- — Request a version with estimated execution time per sub-task if you need to budget for latency or API costs.
Tags
FAQ
What is this prompt for?
How should I customize it?
Are there any limitations?
How is it different from a basic prompt?
Related Prompts
All AI Agents prompts →Multi-Agent Role Designer
Designs a clean division of labor for a multi-agent system, defining each sub-agent's role, boundaries, and handoff protocol so agents don't overlap or conflict.
Multi-Agent Workflow Architect
Design a complete multi-agent system architecture for a complex task, including agent roles, handoff logic, failure handling, and human checkpoints.
Tool Definition Writer for Agents
Writes a clear, well-scoped tool definition (name, description, parameters, and usage guidance) for an AI agent, designed to minimize misuse and ambiguous tool-calling.
Related Skills
Workflow Automation Blueprint
Turns a messy manual process description into a step-by-step automation blueprint with trigger, actions, tools, and edge cases mapped out.
Automation Blueprint Writer
Turns a plain-language description of a repetitive task into a step-by-step automation blueprint, complete with trigger, actions, tools, and edge-case handling.
AI Workflow Architect
Design reliable AI-agent and LLM workflows from a business goal, including steps, tools, prompts, controls, and implementation guidance.
Related Articles
How do I evaluate an AI agent before trusting it?
Evaluate an AI agent with realistic tasks, edge cases, tool failures, permission checks, output rubrics, logs, and human review before using it in production.
AI Agents vs Chatbots vs Automations: What's the Difference?
A clear comparison of chatbots, automations, and AI agents, with examples and a guide to choosing the right one for your task.
How do I add memory to an AI agent or n8n workflow?
Memory can mean chat history, user preferences, retrieved documents, or stored workflow state. It should be scoped, editable, and limited to what the task needs.