AI agents and automation prompts are advanced instructions for building the prompts and logic that power autonomous workflows. On Prompt Dock they cover real engineering tasks: system prompts and personas, tool-use and function-calling instructions, multi-step reasoning chains, agent role definitions, guardrail and error-handling wording, and workflow automation blueprints. Every prompt is human-reviewed before it goes live, so the structure is reliable rather than brittle. Free and premium prompts are both available, and you can reveal any prompt and run it live in the Playground to see how the model behaves before you wire it into an agent, chatbot, or automation.
4 prompts
Give it a prompt you rely on and it builds the test cases that catch a regression when you edit it — normal inputs, edge cases, adversarial ones, and the pass criteria for each, so you can change a prompt without hoping.
Describe an agent workflow and it maps where it will fail in production — loops, silent wrong answers, unsafe actions, and cost blowups — with the guardrail for each and the one that must exist before you ship.
Describe what you want an agent to be able to do and get well-formed tool definitions — names, descriptions, and parameter schemas written so the model picks the right tool, plus the failure modes each one invites.
Describe your documents and the questions users ask, and it recommends a chunking and retrieval strategy that fits them — size, overlap, metadata, and the evaluation that proves retrieval is the problem before you blame the model.
Prompt Dock's AI Agents & Automation category collects them, and every prompt is human-reviewed before it publishes. Reveal the full text, pick free or premium, and run it in the Playground to observe the model's behavior on real input before you build the prompt into an agent or automated workflow.
They cover system prompts and personas, tool-use and function-calling instructions, multi-step reasoning chains, agent role definitions, guardrails, and automation blueprints. Each prompt is scoped to an agent-building task and human-reviewed, so you start from robust, reviewed structure instead of debugging a fragile system prompt yourself.
Claude and GPT both power production agents; Claude is often chosen for careful tool use and long-context reasoning, GPT for broad ecosystem support. Agent prompts note a recommended model, and the Playground lets you run the same system prompt to compare behavior before committing to a model for your build.