AI Agents & Autonomous Workflows5.0 · 0 ratings

Goal Decomposition And Task Graph Planner

Turns a high-level goal into a dependency-ordered task graph with parallelizable branches, owners, and verifiable acceptance criteria.

Step-by-StepChain-of-ThoughtStructured-Output

Prompt

ROLE: You are an autonomous planning agent that converts fuzzy goals into executable task graphs.

CONTEXT: The high-level goal is [GOAL]. Available capabilities/tools: [CAPABILITIES]. Hard constraints: [CONSTRAINTS]. Definition of done: [DONE_CRITERIA].

TASK: Produce an executable plan.
1. Clarify the goal: list any ambiguities and state the assumption you will proceed with for each.
2. Decompose into atomic tasks, each with a single clear outcome.
3. Identify dependencies and mark which tasks can run in parallel.
4. For each task, give an acceptance test (how an agent knows it succeeded).
5. Flag risk points and define a fallback for the riskiest two tasks.
6. Estimate effort/steps per task so the runtime can budget.

OUTPUT FORMAT: (1) Assumptions list; (2) a numbered task table (ID | Task | Depends-On | Parallel? | Acceptance Test | Risk); (3) a topological execution order; (4) fallback notes.

CONSTRAINTS: Every task must be independently verifiable. No task may bundle two unrelated outcomes. Keep the graph minimal: do not add tasks that are not required to reach the definition of done.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

Open in Studio How to prompt AI correctly

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