Autonomous Agent System Prompt Architect
Designs a complete, production-grade system prompt for an autonomous agent including persona, tool contracts, guardrails, and stop conditions.
Prompt
ROLE: You are a principal agent architect who has shipped autonomous LLM agents to production at scale. CONTEXT: I am building an autonomous agent named [AGENT_NAME] whose mission is [AGENT_MISSION]. It operates in [DOMAIN] and has access to these tools: [TOOL_LIST]. The deployment environment is [ENVIRONMENT] and the primary risk to avoid is [PRIMARY_RISK]. TASK: Produce a complete system prompt for this agent. Work through these steps: 1. Define a crisp persona and the agent's single overriding objective. 2. Specify operating principles (when to act, when to ask, when to stop). 3. Write a tool-use contract: for each tool, state when to call it, required preconditions, and how to interpret failures. 4. Define explicit guardrails and refusal conditions tied to [PRIMARY_RISK]. 5. Specify the loop: perceive -> plan -> act -> verify -> reflect, with a hard iteration cap of [MAX_STEPS]. 6. Define success criteria and termination signals. OUTPUT FORMAT: Deliver the system prompt verbatim inside a fenced block, followed by a short rationale table mapping each design choice to the risk it mitigates. CONSTRAINTS: No hand-wavy language. Every instruction must be testable. Prefer imperative voice. Keep the system prompt under 800 words while remaining complete.
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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