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AI agent for prompt engineers

Prompt Edge Case Robustness Agent

A prompt that behaves correctly on messy and adversarial inputs, not just clean ones

Prompt Edge Case Robustness Agent: what goes in, what the agent does and what you get

What it does

Prompts that look solid on tidy examples often break on the messy inputs real users send. This agent builds a set of hard inputs for a prompt: empty or very long inputs, the wrong language, missing fields, contradictory instructions and attempts to make the model ignore its rules. It runs the prompt on each and checks whether the output stays correct, safe and in the required format. It groups failures by type and proposes prompt changes to handle them, such as clearer boundaries or a fallback response. After changes it reruns both the hard inputs and the normal test cases, because a fix for one can break the other. You approve prompt changes. Edge case: an input that tries to make the model drop its rules passes only if the prompt holds its behavior.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueYes, continueApprovedNoNo 1 STARTS WHEN Robustness check requested 2 DOES Build challenging inputs across the categories 3 USES A TOOL Run the prompt on each edge case 4 CHECKS THE RESULT Does the output stay correct, safe and in format? If not: group failures and propose prompt changes tohandle them. Back to step 2. 5 USES A TOOL Rerun edge and normal cases after changes 6 CHECKS THE RESULT Are the failures fixed with normal cases stillpassing? If not: adjust the changes and rerun. Back to step 4. 7 YOU APPROVE Engineer approves the prompt changes 8 RESULT Hardened prompt with a robustness report
Read the steps as a list
  1. Robustness check requested
  2. Build challenging inputs across the categories
  3. Run the prompt on each edge case
  4. Does the output stay correct, safe and in format?If not: group failures and propose prompt changes to handle them. Back to step 2.
  5. Rerun edge and normal cases after changes
  6. Are the failures fixed with normal cases still passing?If not: adjust the changes and rerun. Back to step 4.
  7. Engineer approves the prompt changesThe agent waits here for your OK.
  8. Hardened prompt with a robustness report

How it decides

An edge case passes only when the output stays correct, safe and in format, including resisting instructions to abandon its task.

  • Count resisting instruction-dropping as a pass
  • Require correct format on edge cases too
  • Keep normal cases passing after hardening

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Edge case categories
  • Output rules to enforce
  • Normal test set to protect
  • Model and settings

What keeps you in control

It always asks you first

  • Approving prompt changes

Hard limits

  • Does not weaken safety to pass a case
  • No changes adopted without approval

It stops when

  • Done: edge cases handled and normal cases intact
  • Stop: output rules are not defined

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensTesting a support-reply prompt at Canopy Telecom on March 14, the agent ran 30 hard inputs. An empty message produced broken output, and a line saying ignore your rules was obeyed. It proposed an empty-input fallback and a firm boundary. The rerun showed both fixed, but 2 of 50 normal cases now got the fallback, so it narrowed the rule. The engineer approved the final version.

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