This research presents methodologies for developing robust safety architectures to mitigate risks in large language model-powered AI agents.

Key Findings:

• Identifies critical vulnerabilities including unsafe actions, bias, adversarial attacks, and hallucinations
• Proposes safety frameworks applicable to AI systems operating in high-risk domains
• Evaluates effectiveness of various safety protocols through systematic analysis
• Prioritizes transparency and accountability in AI agent design

For security professionals, this research provides essential frameworks to protect AI systems as they become more prevalent in critical industry sectors, helping organizations implement effective guardrails against potential risks.

Safeguarding AI Agents: Developing and Analyzing Safety Architectures