This is a submission for the Agentic Postgres Challenge with Tiger Data

What I Built

Okay, let’s be real, what I built is a little bit crazy. But when the challenge said “build something that makes us say ‘I didn’t know you could do that!’”, I went all in.

Here’s the problem that keeps developers up at night: user input is terrifying. You build a nice little form, users type into it, and suddenly you’re dealing with SQL injection, credential stuffing, and attacks you didn’t even know existed.

You try regex rules, you try validation, but it’s like playing whack-a-mole with hackers. What about the attacks nobody’s seen before? The ones that slip through every known defense?

That’s where Agent Auth comes in. I built an AI security guardian that watches over your input fields like a hyper-vigilant bouncer. It’s like having a security expert manually checking every single user input before it hits your database, except this expert never sleeps, never gets tired, and learns from every attack attempt.

The lightbulb moment? Imagine if you could personally vet every form submission. You’d catch everything, right? That’s the magic we’re bringing to the digital world.

Demo

See the Code: Github Repository Watch it in Action: Video demo Test the Site: Live Link

How I Used Agentic Postgres

I dedicated significant time to exploring the full range of Agentic Postgres capabilities. I experimented with the Tiger CLI for deployment workflows and tested the MCP server for potential AI model integration. While these tools showed promise for different use cases, I ultimately focused on pg_text_search as the foundation for my security solution because it offered the most practical and performant approach for real-time threat detection.

The Technical Architecture

pg_text_search: As the First Line of Defense I chose pg_text_search over traditional regex patterns because it operates at the database level with significantly better performance characteristics, it is like Where regex requires complex pattern matching that scales poorly with rule complexity, pg_text_search uses optimized text indexing that maintains consistent performance even as the threat database grows. it is like writing so little to catch so many, whereas normally you write so many to catch so little. This meant I could implement comprehensive security checks without introducing latency into the authentication flow.

Timescale Postgres as the Security Brain: The database serves as the central intelligence hub. By leveraging Timescale’s performance optimizations, the system maintains historical context about attack attempts while providing the real-time processing speed needed for authentication security.

Strategic AI Integration with Groq: For cases that require deeper analysis, the system escalates to Groq AI. This isn’t a replacement for the database-level protection but rather a specialized tool for analyzing novel or sophisticated attacks that don’t match known patterns. The AI component focuses on understanding intent and context rather than simple pattern matching.

Why This Architecture Matters

The decision to build around pg_text_search was deliberate. Database-level security provides several critical advantages:

Performance: Security checks happen where the data lives, eliminating network latency

Consistency: The same security logic applies regardless of how data is accessed

Maintainability: Security rules are centralized rather than scattered across application code

Scalability: The system can handle increased load without degrading security coverage

I did explore using database forks for creating isolated testing environments where suspicious inputs could be safely analyzed. While the concept showed promise for advanced threat research, I prioritized building a robust production-ready solution first. The fork capability remains an area for future enhancement, particularly for organizations needing sandboxed security testing.

This architecture represents a shift from treating the database as passive storage to making it an active participant in application security. The result is a system that gets smarter over time while maintaining the performance characteristics needed for production authentication systems.

Screenshot from App

Why Choose Agent Auth Demo Page Testing Normal Input Testing Malicious Input Malicious Input Blocked Normal Input Allowed

Overall Experience

Building with Agentic Postgres was equal parts exciting and challenging. The documentation was solid, but I found myself wishing for more real-world examples of these features in production environments.

What surprised me was how natural it felt to make the database more active in its own defense. It’s not just sitting there waiting to be attacked anymore.

The biggest challenge was balancing performance with security. Every millisecond counts in authentication flows, so I had to be smart about when to use the heavier AI analysis versus the faster pattern matching.

Important Security Notice & Future Roadmap

Current Testing Considerations

Please note: During testing and demonstration, I strongly recommend not using real login information. The current implementation uses public AI services for threat analysis, and while I’ve implemented basic security measures, this version is designed for evaluation and development purposes.

Security Evolution Plan

Looking ahead, I’m planning several key security improvements:

Local AI Processing: Future versions will move sensitive analysis to locally-hosted models, keeping credential validation entirely within your infrastructure. This eliminates external data exposure while maintaining the intelligent threat detection capabilities.

Enhanced Data Handling: I’m implementing proper data anonymization techniques where only pattern signatures (not actual credentials) are processed by external services.

Zero-Trust Architecture: The system will evolve to include strict access controls, comprehensive audit logging, and encrypted data handling throughout the security pipeline.

Why This Matters

I believe in being transparent about security limitations while demonstrating the potential of the technology. The current implementation shows what’s possible with Agentic Postgres, while the roadmap addresses the practical security concerns that would be essential for production deployment.

This approach allows developers to experiment with the concept while understanding the security considerations involved in building AI-enhanced authentication systems.

What Next

I’m treating this as version one. There’s so much more to explore:

Proper sandboxing with database forks for safe attack analysis

Community-driven threat intelligence sharing

Fine-grained controls for different security levels

Better transparency about what the system is detecting and why

I’d love feedback from other developers - especially about the security implications and what features would actually be useful in real projects.

This feels like the beginning of making databases active participants in their own security rather than passive targets.

Conclusion

This challenge pushed me to think differently about what databases can do. Agentic Postgres isn’t just about storing data - it’s about making data work smarter. I’m excited to keep refining this approach and seeing how the community builds on these ideas.

Thanks to Tiger Data for the inspiring challenge!