Vigilagent

VigilAgent was inspired by a simple question: what if an AI team could think and work like a real penetration testing team? As cyberattacks become more advanced, organizations struggle to keep up because manual penetration testing takes time, requires experienced professionals and cannot always scale with demand. At the same time, traditional vulnerability scanners often stop at detecting possible issues without validating whether they are actually exploitable. We built VigilAgent to solve this problem by creating an autonomous multi-agent AI platform where specialized agents work together to perform reconnaissance, identify vulnerabilities, plan attack paths, validate exploits, collect evidence and generate clear remediation reports. Each agent has a specific responsibility and collaborates through an orchestration layer that manages communication, shared memory and task execution. One of the biggest challenges was making multiple AI agents coordinate effectively while maintaining context throughout long testing sessions and reducing hallucinations by verifying every important finding with real evidence. Integrating AI reasoning with existing security tools and ensuring safe autonomous execution also required careful design. Working on this project taught us that building reliable AI systems is not just about using powerful language models. It is about designing agents that collaborate effectively, validate their decisions and produce results that security teams can trust. VigilAgent shows how coordinated AI agents can make penetration testing faster, more scalable, and more practical while allowing human security professionals to focus on the most critical decisions.

Built With

• css
• docker
• fastapi
• gemini
• javascript
• langchain
• nmap
• openai
• postgresql
• python
• react
• redis
• subdomain
• supabase