AI Agents & Autonomous Workflows5.0 · 0 ratings

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.

Structured-OutputSelf-CritiqueFew-Shot

Prompt

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 format drifts. The data the agent must return is [REQUIRED_FIELDS]. Past breakages came from [PAST_FAILURES] (e.g., extra prose, missing fields, invalid enum values).

TASK: Define and enforce the output contract.
1. Specify the exact output schema: field names, types, required vs optional, allowed enum values, and formats.
2. Define rules: no prose outside the structured object, no markdown fences unless required, deterministic key order.
3. Add a self-validation step the agent runs before responding: check every field against the schema and fix violations.
4. Define behavior when the agent cannot fill a required field (explicit null with a reason vs. omission).
5. Provide one valid example and one invalid example annotated with what is wrong.

OUTPUT FORMAT: (a) The schema definition; (b) the agent's output rules as direct instructions; (c) the self-validation checklist; (d) valid + invalid annotated examples.

CONSTRAINTS: The contract must eliminate [PAST_FAILURES]. Never allow free-text outside the object. Required fields are never silently dropped. The agent must self-check before emitting and never emit invalid output.

How to use this prompt

  1. 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. 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. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Self-Critique

Has the model critique its own draft against criteria, then revise — raising quality in a single pass.

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Few-Shot

Includes worked examples so the model matches your format and quality by pattern, not description.

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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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