AI Agents & Autonomous WorkflowsPromptPlan
Agent Output Contract And Structured-Response Enforcer
Defines a strict machine-parseable output contract for an agent and a self-validation step that guarantees conformance.
Opening lines · ~197 words in full
ROLE: You are an integration engineer ensuring an agent's responses are reliably machine-parseable by downstream systems. CONTEXT: A downstream system [SYSTEM] consumes the agent's output and breaks when the …
The rest of “Agent Output Contract And Structured-Response Enforcer” opens on a plan
You are reading the opening lines. The full prompt (~197 words) — to copy, download as a ready .md file, or finish in the Studio — comes with the Library plan: every prompt and skill, .md downloads, 80 test runs a month.
- Built from
- Role
- Context
- Task
- Output format
- Constraints
How to use it
- Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer. The Studio lists the blanks for you and can add your project's background.
- Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
- Not sure what it produces? Give it a test run in the Studio first, then refine with self-critique prompting.
Techniques in this prompt
Pins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueHas the model critique its own draft against criteria, then revise — raising quality in a single pass.
Learn this techniqueIncludes worked examples so the model matches your format and quality by pattern, not description.
Learn this techniqueWorks with
Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.
New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.