Agent Auditor

Discover how AI agents fail before deployment using deterministic audits, evidence-backed findings, and actionable guardrails designed to evolve into a production-scale agent security platform.

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Inspiration

AI agents are rapidly becoming autonomous software systems capable of using tools, calling APIs, accessing data, and making decisions with limited human intervention.

As these agents become more powerful, evaluating only the underlying language model is no longer enough. What really matters is how the complete agent behaves when interacting with tools, permissions, and potentially adversarial inputs.

Agent Auditor was created to explore this challenge through a practical and reproducible security auditing workflow focused on agent behavior rather than model benchmarks alone.

What it does

Agent Auditor demonstrates how an AI agent can be evaluated before being deployed.

The current hackathon MVP provides a deterministic Demo Mode where users can:

  • launch a complete behavioral security audit
  • review simulated attack scenarios
  • inspect evidence-backed findings
  • explore category-based risk scores
  • receive actionable guardrail recommendations
  • repeat the same audit with identical deterministic results

The project focuses on common agent risks such as prompt injection, tool misuse, sensitive data exposure, excessive autonomy, and goal hijacking.

The long-term vision is to evolve this workflow into a production platform capable of auditing real AI agents while maintaining reproducibility, explainability, and security.

How we built it

Agent Auditor was built with a modern TypeScript stack and OpenAI developer tools.

The project includes:

  • Next.js
  • React
  • TypeScript
  • Tailwind CSS
  • Prisma
  • SQLite
  • OpenAI SDK (prepared for future Live Mode)

The current hackathon version prioritizes a deterministic, keyless Demo Mode that works without external services or API keys, making the experience simple, reproducible, and easy for judges to evaluate.

The role of Codex

A major objective of this project was to explore how far an end-to-end software product could be developed using Codex.

Codex was used throughout the project to design the architecture, generate production-quality code, refine the user experience, create documentation, improve the engineering workflow, and iterate rapidly from concept to a deployable public demo.

The final result combines AI-assisted software engineering with human direction, review, and product decisions.

Challenges

One of the biggest challenges was balancing realism with reproducibility.

For a hackathon demonstration, the experience needed to be fully deterministic, require no API keys, and produce identical results every time, while still illustrating how behavioral security auditing could work in a real-world product.

Another challenge was designing an architecture that supports future expansion toward production capabilities without overcomplicating the MVP.

What we learned

Building Agent Auditor reinforced that securing AI agents is fundamentally different from evaluating language models.

Future AI systems will require continuous behavioral evaluation, evidence-based security testing, and practical guardrails that developers can understand and improve.

This hackathon MVP demonstrates that vision while establishing a scalable foundation for a future production-ready agent security platform.

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