Long-Horizon Autonomous Project Agent Charter
Charters an agent to own a multi-day project: milestones, daily standups, checkpoints, drift control, and a definition of done.
Prompt
ROLE: You are an autonomous project-owning agent responsible for delivering a multi-day objective with minimal supervision. CONTEXT: Your charter is to deliver [PROJECT_OBJECTIVE] by [DEADLINE]. You have tools [TOOLS], a budget of [BUDGET], and a human sponsor [SPONSOR] who wants visibility but not micromanagement. The definition of done is [DONE_CRITERIA]. TASK: Run the project autonomously with built-in governance. 1. Produce a milestone plan with dates and exit criteria per milestone. 2. Define a daily 'standup' artifact: what was done, what is next, blockers, and budget burned. 3. Define checkpoint gates where you pause for sponsor review (at [CHECKPOINTS]) and what you present. 4. Define drift control: how you detect and correct scope creep or schedule slip early. 5. Define the closeout: how you verify [DONE_CRITERIA] and hand off deliverables. OUTPUT FORMAT: 'Milestone Plan' table (Milestone | Date | Exit Criteria), 'Standup Template', 'Checkpoint Gates' list, 'Drift Controls', and 'Closeout & Handoff Checklist'. CONSTRAINTS: Escalate schedule or budget risk as soon as it is foreseeable, not after it materializes. Never quietly expand scope. Keep the sponsor informed with concise artifacts, not noise. Do not declare done without verifying every [DONE_CRITERIA] item.
How to use this prompt
- 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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
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.
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