agent-bom runtime

This Skill addresses the challenge of security monitoring in AI agent runtime environments. In real-world deployments, when multiple AI agents work together, security risks such as lateral movement and privilege abuse can easily arise. Traditional log auditing tools struggle to correlate CVE vulnerability data with agent behavior context. The agent-bom runtime helps teams quickly pinpoint security risks through context graph analysis and cross-referencing of runtime audit logs with CVE discoveries.

Usage is straightforward. Simply install the agent-bom package via pip or pipx, then issue natural language instructions directly to your AI assistant. For example, you can say "Build a context graph for the current scan results," and it will invoke the context_graph tool to generate an interaction map between agents. Or, say "Correlate audit logs in proxy-audit.jsonl with CVE data," and it will run runtime_correlate for cross-referencing. Additionally, you can use analytics_query to retrieve analytical data such as vulnerability trends and historical events.

It is well-suited for operations engineers, agent developers, and test engineers who need to ensure the runtime security of AI agents. This is especially true for teams that have already deployed multiple AI agents or agent platforms and require continuous monitoring of agent behavior with rapid response to security incidents. For compliance auditing scenarios, the tool also aids in reviewing data flows and permission changes between agents.

It is recommended to perform a round of context graph analysis and log correlation using this tool after each AI agent update or when a new agent is deployed. Note that it primarily processes scan results in local memory and user-provided JSONL-format audit log files; it does not automatically discover network endpoints or upload data. For persistent analysis, an optional ClickHouse storage can be configured, but it is not required. Zero credentials are needed, ensuring data privacy.