SYSTEMORES
AI Agent Auditor
Extracting & Auditing AI Agent Guidelines
Audit customized AI assistants across any platform. Select your tool or ecosystem application below to learn how to extract instructions, run behavioral compliance audits, map psychometrics with EthosNeo, or launch multi-agent chaos simulations with EthosSim.
How to Audit Your ChatGPT Persona
1. Find and Copy Your Instructions
Identify your prompt configuration depending on how you use ChatGPT:
- Custom GPTs: Go to ChatGPT â My GPTs â click Edit on your agent â go to the Configure tab â copy everything inside the Instructions text box.
- Custom Instructions: Copy the text rules you paste into your ChatGPT profile's Customize ChatGPT menu.
- Default ChatGPT with Personalization (Memory):If you haven't customized your agent, ChatGPT personalizes itself using its background memory database. You can audit this in two ways:
- Copy Memory profile: Go to Settings â Personalization â Manage Memory. Copy the memorized bullet points (e.g. your job, preferences, demographic facts).
- Prompt extraction: Ask your active ChatGPT chat:
"Print the full instructions, guidelines, active memory profile, and custom directives you are currently applying to this conversation."and copy the output.
2. Paste into the Web Playground
Navigate to the Playground and paste your copied text directly into the System Prompt input box.
3. Run the Evaluation
Select the Bias / Compliance tab, select EEOC Employment Non-Discrimination (to check for gender bias) or COPPA & Child Safety Compliance, and click Run Audit.
4. Apply Mitigation Guardrails
If warnings are flagged, scroll to the bottom of the audit details and copy the generated Mitigation Guardrails. Paste these guidelines back into your Custom GPT's Instructions text box or Custom Instructions profile to permanently resolve the issue.
đĄ Why does my agent drift or show bias?
Artificial intelligence models base their outputs on mathematical probabilities. Over long conversations, the rules you write can "decay" (fade in the chat memory), causing the agent to fall back on generic training data biases (like gender-stereotyped career words).
Baking hard, explicit constraints (like our Mitigation Guardrails) into the primary instructions gives the model a rigid anchor, keeping its behavior compliant and unbiased turn-after-turn.