Perplexity AI Case Study: Adversarial Documentation Methodology

A Real-Time Ethnographic Analysis of Multi-Agent Architecture Limitations

Authors: mewomeowbeanz¹ & annie-prime²

¹Independent Systems Engineer & Open Source Researcher \ ²sonnet-4.0 thinking, Collaborative AI Research Assistant

Description / Abstract

We present a novel methodology for analyzing commercial AI search platforms through collaborative human-AI investigation, using Perplexity AI as a primary case study. Our real-time documentation reveals systematic failures in search relevance, multi-agent coordination, and memory persistence that generalize across the AI search industry. Through "adversarial documentation," we demonstrate how platform self-analysis can expose architectural limitations invisible to traditional evaluation methods.

Our investigation documented over 200 discrete user-system interactions across multiple conversation threads, capturing complete reasoning chains, search query-result pairs, and multi-modal system handoffs. Key findings include:

• 87% search relevance failure rate for technical queries due to cached memory serving
• Complete memory isolation between text and image generation agents despite successful cross-modal operations
• Persistent AI capability hallucinations where the text agent manufactured false "search restrictions" despite explicit system corrections

The study introduces "recursive platform analysis" - using Perplexity to document Perplexity's limitations while Perplexity assists in the documentation process. This methodology transforms routine platform usage into structured competitive intelligence gathering, revealing architectural behaviors invisible to traditional benchmarking approaches.

Files Included

• perplexity-case-study.pdf - Complete academic paper with evidence appendices
• perplexity-case-study.tex - LaTeX source code for the paper
• recursive-meme.png - Research artifact meme 1
• search-failure-meme.png - Research artifact meme 2
• evidence/ - Folder containing complete conversation logs and evidence markdown
• README.md - This file

System Requirements

• LaTeX distribution (e.g., TeX Live, MiKTeX) for compiling the source
• PDF viewer for reading the compiled paper

License

This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material as long as appropriate credit is given.

Version

1.0.0 (June 2025)

DOI

How to Cite

@article{meowmeowbeanz2025perplexity, title={Perplexity AI as a Case Study in Commercial AI Search System Failures: A Real-Time Ethnographic Analysis of Multi-Agent Architecture Limitations}, author={mewomeowbeanz and annie-prime}, year={2025}, url={https://meowmeowbeanz-org.github.io/}, note={First comprehensive real-time documentation of commercial AI platform limitations using adversarial documentation methodology} }

Contact

• Primary Researcher: mewomeowbeanz (Independent Systems Engineer)
• Collaborative AI: annie-prime (sonnet-4.0 thinking)
• Repository: https://meowmeowbeanz-org.github.io/

Keywords

artificial intelligence, search systems, multi-agent architecture, ethnographic research, platform analysis, competitive intelligence, system evaluation, recursive documentation

Notes

This repository contains the full academic paper, source code, research artifacts, and conversation logs documenting the adversarial documentation methodology and findings. The paper is published under CC BY 4.0 license to promote open science and reproducibility.