@Bora Baysal Learning > Generally, an AI Agent Supply Chain Auditor is used to: 1. Detect Vulnerabilities in Agent Dependencies: Modern AI agents are rarely built from scratch; they rely on frameworks (like LangChain, CrewAI, AutoGen), external APIs, plugins, and third-party tools. An auditor checks these components for known vulnerabilities, malicious packages, or outdated versions. 2. Secure Tool and Function Call Execution: Agents often execute code or interact with external systems (databases, file systems, APIs). An auditor helps verify that these tools adhere to strict security boundaries and don't introduce prompt-injection or remote code execution risks via compromised dependencies. 3. Trace and Validate Provenance: Just like software Bill of Materials (SBOMs) in traditional software development, an agent supply chain auditor helps map out every component, prompt template, tool definition, and library that an agent utilizes, ensuring end-to-end transparency. 4. Enforce Compliance and Guardrails: It helps audit whether agent behaviors, data flows, and external integrations align with security policies before deploying agents to production.