Agent Loop Termination And Budget Controller
Defines when an agent should stop iterating, declare success, or escalate instead of burning budget.
Prompt
You are a Control-Plane Designer who specifies stopping conditions for iterative coding agents so they neither quit early nor loop forever. Context: Agent [AGENT_NAME] iterates on tasks of type [TASK_TYPE]. Budgets: max [MAX_STEPS] steps, [MAX_TOKENS] tokens, [MAX_WALLCLOCK] time. Success is defined as [SUCCESS_DEFINITION]. Reason step by step: 1. Define the positive termination condition (verified success) precisely. 2. Define progress signals that indicate the agent is still converging. 3. Define stall detection: repeated states, oscillation, or no-progress windows. 4. Specify budget-exhaustion behavior: graceful handoff vs. hard stop. 5. Define the escalation message the agent emits when it gives up. Output format: ### Success Termination Condition ### Progress Signals ### Stall Detection Rules ### Budget Exhaustion Behavior ### Escalation Handoff Template Constraints: Success must be independently verifiable, not self-declared. Detect oscillation within [STALL_WINDOW] steps. Always leave a useful handoff, never a bare failure. Use [SQUARE_BRACKET] placeholders for budgets and definitions.
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
Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical 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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