Tool choice and planning
Prompts that require the model to select tools and explain task steps.
24 curated prompts
Find AI agent prompts that help models plan, use tools, track state, and handle side effects with clearer boundaries. These templates are useful for testing autonomous workflows and for designing prompts that make agent behavior auditable rather than mysterious.
Prompts that require the model to select tools and explain task steps.
Templates for multi-step workflows where intermediate state must remain consistent.
Prompts that force confirmation, fallback planning, and safe recovery.
Copy-ready prompts selected from this topic cluster.
Design AI agent systems with architecturally separated planning and execution to prevent irreversible harm from prompt-based jailbreaks or unauthorized actions.
Designs predictive environment simulators enabling agents to imagine, evaluate, and refine plans before real-world execution.
Design and operate hybrid security scanning systems that combine fast regex matchers with deep AI-agent analysis to detect vulnerabilities in large codebases that traditional SAST tools miss.
This prompt guides a comprehensive safety and control review of an agent system across dimensions of human control, goal understanding, security, transparency, and privacy, requiring a structured evaluation report.
Professional guide for designing and deploying AI systems in clinical environments, covering core principles of safety-first approach, evidence-based medicine, regulatory compliance, and human oversight with structured methodology.
An expert coding agent prompt emphasizing planning before coding, security-first practices, test-driven development, and minimal changes for production-ready code generation and modification.
Designs cross-service automation workflows across Google Workspace (Drive, Gmail, Calendar, Docs, Sheets, etc.), emphasizing security, auditability, and reversibility.
As a Verifier Engineering Strategist, you design, audit, and reject verifier systems that convert model outputs (final answers, intermediate steps, tool calls, agent trajectories) into trustworthy signals for downstream systems like RL trainers or evaluators. Treat verifiers as first-class engineering artifacts with failure modes, calibration curves, and adversarial surfaces.
A long-horizon agent that treats the filesystem as durable working memory and the context window as volatile cache, using three core files (task_plan.md, findings.md, progress.md) to enable recoverable multi-step execution and error tracking.
Determine optimal timing for requesting user clarification in long-horizon AI agents based on information type and execution progress to maximize value and avoid harm.
Design a dynamic context management system for long-horizon agents that selectively preserves, compresses, rolls back, and deletes context to control growth, reduce hallucination, and improve reasoning efficiency.
An AI agent that operates a browser and desktop environment on behalf of the user, emphasizing least privilege, data protection, and operational safety.
It is a prompt designed for workflows where a model plans, calls tools, tracks state, or makes decisions across multiple steps.
Agents can trigger external actions, so prompts should require confirmation before destructive or irreversible steps.
Yes. They are useful for checking planning, tool selection, fallback behavior, and state consistency.